Circadian Clocks: Clock Genes, Clock Cells and Clock Circuits

Part 1: Clock Genes, Clock Cells and Clock Circuits

00:00:07.16 Hello. 00:00:08.23 My name is Joe Takahashi. 00:00:10.05 I'm at UT Southwestern Medical Center 00:00:13.15 in Dallas, 00:00:14.24 and the Howard Hughes Medical Institute. 00:00:16.17 And today I'll be talking about 00:00:18.13 circadian clocks 00:00:21.00 in three different lectures. 00:00:24.07 So, as all of us know, 00:00:27.24 we live on a planet 00:00:29.25 that is governed by the diurnal cycle, 00:00:33.14 which is caused by the Earth's rotation, 00:00:36.14 and this has led to 00:00:38.28 the evolution of biological clocks 00:00:42.13 in virtually all living organisms, 00:00:45.24 to anticipate the changes in the environment. 00:00:49.27 And what I'd like to do today 00:00:51.20 is really to give you an introduction 00:00:53.17 to biological clocks, 00:00:55.13 first talk about the genes, 00:00:57.27 and then about the clocks in cells, 00:01:00.22 and then how they're organized in the body. 00:01:04.11 So, to get us all oriented, 00:01:06.23 perhaps the most familiar 00:01:10.23 biological rhythm to us 00:01:12.07 is our sleep-wake cycle, 00:01:15.02 and so this is an example 00:01:17.13 of a sleep-wake record 00:01:19.18 from a young German medical student 00:01:21.23 who volunteered for an experiment 00:01:23.28 in the 1960s 00:01:25.19 in the Max Planck Institute 00:01:28.05 in Andechs, Germany, 00:01:30.00 where they have two underground bunkers 00:01:32.09 where people can go into 00:01:36.18 what we call temporal isolation. 00:01:38.13 And so, in this record here, 00:01:40.19 the blue bars show 00:01:42.18 when the German medical student is awake 00:01:44.16 and the yellow bars show 00:01:46.15 when he's sleeping, 00:01:47.24 and then there's a little white arrow 00:01:49.21 that shows the low point 00:01:51.26 in the body temperature rhythm 00:01:53.27 of our circadian rhythm, 00:01:55.19 which occurs about three hours 00:01:57.27 before we wake up, 00:01:59.25 and so this experiment, 00:02:01.12 which actually lasted about 40 days, 00:02:05.23 shows two really important features 00:02:08.00 of circadian rhythms. 00:02:09.07 So, in the first week 00:02:12.16 the student is living in the apartments, 00:02:14.00 the door is open, 00:02:15.13 so he's exposed to the 24-hour light schedule, 00:02:18.14 and what you can see is that 00:02:22.09 he's waking up at about 8 o'clock in the morning, 00:02:26.02 real-time, 00:02:28.03 shown at the bottom of this graph, 00:02:29.27 and then he goes to sleep around midnight, 00:02:35.02 each day for about a week. 00:02:36.23 But then at this point right here, 00:02:39.26 the door to that apartment is closed 00:02:41.16 and he goes into temporal isolation, 00:02:44.20 shown in this green area here. 00:02:47.20 And this shows the first feature 00:02:49.09 of these circadian rhythms, 00:02:50.22 and that is you can see that 00:02:52.17 his sleep-wake pattern continues, 00:02:55.17 but there's a difference. 00:02:56.26 The timing of that pattern 00:02:59.13 drifts to later and later times each day. 00:03:02.22 So, down at the bottom of the record, 00:03:04.19 he's waking up every 25.4 hours, 00:03:08.09 instead of every 24 hours. 00:03:10.19 Okay? 00:03:11.22 So, that shows the first feature of circadian rhythms. 00:03:14.12 They are self-generated. 00:03:16.05 They're endogenous to our bodies, 00:03:19.05 and they're not precisely 24 hours. 00:03:22.08 The second feature is actually shown in the bottom here, 00:03:25.03 where he continues to live 00:03:28.17 in the apartment, 00:03:29.23 but the door is open again, 00:03:31.10 and he's exposed to the 24-hour environment, 00:03:34.03 and he then goes back to 00:03:35.27 a very regular schedule 00:03:37.05 where he wakes up actually about 8 o'clock in the morning 00:03:39.11 and goes to sleep at midnight. 00:03:43.06 And this shows the second feature of rhythms, 00:03:44.25 and that is that even though they're endogenous, 00:03:48.02 they're still 00:03:50.01 regulated or synchronized 00:03:51.29 by the external environment, 00:03:53.26 so now the period of his rhythm 00:03:56.28 matches the period of the Earth's rotation, 24 hours, 00:04:01.04 and that process is called entrainment, 00:04:04.15 which involves two features. 00:04:06.23 One is period control 00:04:09.02 -- so, our rhythms adopt a 24-hour cycle -- 00:04:12.01 and the other is phase control, 00:04:15.01 where he wakes up at 8 o'clock in the morning, again, 00:04:18.23 with the same phase as he had before. 00:04:21.10 Now, there's on curious feature in this record, 00:04:24.02 and that is that on this graph, 00:04:26.15 his phase plot is actually 00:04:30.07 on the second 24-hour cycle 00:04:32.13 of the graph. 00:04:33.22 Instead of waking up at 8 o'clock over here, 00:04:36.17 he's waking up at 8 o'clock over here. 00:04:40.08 So, why did that happen? 00:04:42.03 Well, it turns out that 00:04:44.09 while he was in isolation, 00:04:46.21 he went through one fewer biological cycle 00:04:50.29 than the number of real days 00:04:52.29 that actually passed during that time period, 00:04:56.05 so he lost a day. 00:04:58.08 So anyway, 00:04:59.29 just an example of our most familiar 00:05:02.06 biological rhythm. 00:05:04.06 Now, in humans, 00:05:08.21 the principle synchronizer of our rhythms is light. 00:05:13.02 The light cycle is mediated 00:05:15.05 by special photoreceptors in our eyes, 00:05:17.28 in cells that are called 00:05:20.15 intrinsically photoreceptive ganglion cells, 00:05:22.08 which project down the optic nerve 00:05:24.04 into these two yellow wing-like structures 00:05:27.15 in the hypothalamus, 00:05:29.28 which are called the suprachiasmatic nuclei, 00:05:32.22 and within each nucleus 00:05:34.23 there are about 10,000 neurons, 00:05:36.22 each of which is capable of 00:05:40.11 generating circadian rhythms 00:05:42.22 in a cell-autonomous manner. 00:05:45.01 And so, even in humans and mammals, 00:05:48.03 the fundamental unit 00:05:50.16 for generation of circadian rhythms 00:05:53.01 is the cell. 00:05:56.07 Now, the origin of 00:06:01.07 what we would call the modern era 00:06:03.20 of the genetics of circadian rhythms 00:06:05.05 really was started by these two individuals, 00:06:07.09 Ron Konopka and Seymour Benzer, 00:06:10.08 and this is a picture of Ron and Seymour 00:06:13.28 back in 2000, 00:06:16.27 and it turned out what Ron did 00:06:20.02 in the '60s 00:06:21.10 was he undertook a genetic screen 00:06:23.15 in fruit flies 00:06:25.17 and isolated mutations 00:06:28.05 that changed the circadian rhythms of those flies. 00:06:33.12 He isolated three mutants 00:06:35.15 -- one that abolished the rhythm, 00:06:37.04 one that shortened the rhythm, 00:06:38.17 and one that lengthened the rhythm -- 00:06:40.28 and incredibly these three mutants 00:06:44.05 were mapped to the same locus, 00:06:47.01 the same gene, 00:06:48.24 and they named that gene the period gene. 00:06:52.09 Now, since that discovery, 00:06:55.09 which was made in 1971, 00:06:59.15 many different groups have tried to isolate genes 00:07:03.19 of a similar manner in mammals, 00:07:07.15 but we all failed until the late '90s. 00:07:11.23 And so it wasn't possible 00:07:14.15 in those early days 00:07:15.26 to go from the Drosophila period gene 00:07:17.27 to the mouse or human period gene 00:07:20.03 very easily, 00:07:22.05 using DNA sequence homology. 00:07:24.10 And so, in the early '90s, instead, 00:07:28.12 we decided to take a step back 00:07:32.20 and to not look for similar genes 00:07:35.01 but instead to look for clock mutants, 00:07:39.00 but instead of using fruit flies, 00:07:41.18 we would do the same kind of experiment 00:07:43.27 that Konopka and Benzer did, 00:07:46.09 but we would do those experiments in mouse. 00:07:53.16 And so, using the mouse, 00:07:56.14 we were able to conduct a genetic screen, 00:07:58.07 which was done by Martha Vitaterna, 00:08:01.03 and this illustrates 00:08:03.11 the products of that very first screen 00:08:06.02 we conducted in the early '90s, 00:08:08.20 where we used the activity rhythm of a mouse, 00:08:12.17 shown here... 00:08:14.06 this is the record of a normal mouse, 00:08:15.25 the black bars indicate when the mouse is active, 00:08:20.26 and in this case the plot is double-plotted 00:08:23.14 so that you can see the pattern better. 00:08:26.05 The green and white bar at the top 00:08:28.11 indicates the light cycle 00:08:30.03 to which the mouse was exposed 00:08:31.24 at the beginning of the record, 00:08:33.04 but then in the bottom of this record, 00:08:35.10 right here, 00:08:36.27 the mouse is in constant conditions, 00:08:38.26 and so we can measure the period 00:08:40.28 of its biological clock. 00:08:42.27 And in this screen, 00:08:46.03 Vitaterna found this particular mouse, 00:08:49.11 shown here in the bottom right, 00:08:52.07 which has a period of 25 hours. 00:08:54.14 It was mouse number 25. 00:08:57.19 And it turned out this period lengthening 00:09:01.09 was caused by a single gene mutation, 00:09:04.16 and so we named this mutation Clock, 00:09:07.21 and this shows 00:09:10.20 the effect of that mutation 00:09:12.24 on the periodicity of the rhythm. 00:09:14.07 So, these are actually grandchildren 00:09:16.11 of that founder mouse. 00:09:17.23 This is a wild type, normal mouse, 00:09:20.20 two mice that carry one copy of the mutant gene, 00:09:26.12 and then, at the bottom, 00:09:28.02 this is the record of a mouse 00:09:30.01 that has two mutant copies of the gene, 00:09:32.03 or homozygous mutant Clock. 00:09:34.26 And this mouse is very unusual, 00:09:36.28 because it has a period of its rhythm 00:09:40.19 that's 28 hours long. 00:09:42.18 It wakes up 4 hours later each day. 00:09:46.05 And so, using this mouse, 00:09:50.20 we were then able to try to identify the gene. 00:09:54.14 Now, back then, in the '90s, 00:09:57.25 this was still a difficult task. 00:09:59.23 The mouse genome is comparable 00:10:02.14 to the human genome, 00:10:04.01 there are about 20 chromosomes. 00:10:05.22 In the early '90s, 00:10:07.05 we had only about 300 genetic markers. 00:10:10.06 And of course, today, 00:10:12.10 we have genome sequence, 00:10:15.01 but back then there was no genome sequence, 00:10:19.04 and so we were working in the blind, 00:10:23.11 if you will. 00:10:24.27 And so my laboratory, 00:10:27.04 shown here in this picture 00:10:29.10 taken around 1997, 00:10:31.03 worked together as a team 00:10:33.09 over a three year period 00:10:36.13 to actually isolate the gene 00:10:38.16 using a method that we call positional cloning, 00:10:41.21 which today I sort of call as 00:10:44.11 the Geneticist's Genome Positioning System, 00:10:47.08 or GPS, 00:10:49.17 where we just try to 00:10:51.22 use markers in the genome 00:10:53.18 and hone in 00:10:55.10 and try to find the location of that gene. 00:10:59.10 And so the first step in this process 00:11:02.10 is what's called genetic mapping, 00:11:04.11 and this is shown at the very top, here, 00:11:06.12 in these blue bars, 00:11:08.01 which represent a region 00:11:10.00 of mouse chromosome 5 00:11:12.12 where we used DNA markers 00:11:15.07 and find whether or not they're associated 00:11:19.23 with the mutant phenotype or not. 00:11:21.10 And using this process, 00:11:22.20 which is called genetic mapping, 00:11:24.28 we can estimate the position of the gene 00:11:27.03 in a chromosomal location. 00:11:30.21 Now, back then, of course, 00:11:32.08 unfortunately none of the genes here were known, 00:11:35.04 and in fact the DNA in this region 00:11:37.27 wasn't even cloned, 00:11:39.25 and so shown below 00:11:42.22 are what are called physical maps, 00:11:44.20 which are large fragments of DNA, 00:11:47.21 shown in green and yellow, 00:11:50.06 which represent clones of DNA 00:11:53.07 that we had to isolate to find this region. 00:11:58.16 And so we were able to 00:12:02.29 cover the region in both the green clones, 00:12:05.26 which are called yeast artificial chromosomes, 00:12:08.20 or BAC clones, 00:12:10.05 which are called bacterial artificial chromosomes, 00:12:12.11 in yellow. 00:12:14.19 Now, the breakthrough for us 00:12:19.25 came through a functional approach, 00:12:23.04 where we took a mutant mouse 00:12:27.23 and we were able to then 00:12:30.23 inject DNA, large fragments of DNA from that region, 00:12:33.28 to test whether they might 00:12:36.22 be able to rescue the mutation 00:12:39.24 in a Clock mutant mouse. 00:12:41.28 So, this shows you the general idea 00:12:44.08 of the experiment. 00:12:45.11 We're injecting DNA into the eggs 00:12:46.28 and then we recover mice 00:12:50.08 that are carrying these 00:12:52.16 large bacterial artificial chromosome clones, 00:12:55.13 and we then see whether 00:12:58.16 they can rescue, or fix, or repair 00:13:01.08 the mutation. 00:13:02.19 And these are the results of that experiment. 00:13:04.13 On the left are shown 00:13:06.02 four activity records of Clock mutant mice, 00:13:08.16 as you've seen before, 00:13:09.27 that have the long period 00:13:12.01 and loss of rhythm phenotype, 00:13:14.03 and on the right are 00:13:17.05 four Clock mutant mice 00:13:18.28 that are carrying 00:13:21.07 this very large transgenic fragment 00:13:23.07 that completely rescues the behavior. 00:13:25.28 So, this was a very important result for us 00:13:28.20 because it told us the gene 00:13:30.10 was contained in that piece of DNA. 00:13:32.20 And so, 00:13:35.00 going back to that physical map, 00:13:36.26 the yellow clones, here, 00:13:40.04 are the pieces of DNA 00:13:43.02 that could rescue the mutant phenotype 00:13:45.12 in a mutant mouse. 00:13:47.01 They're pretty large; 00:13:49.04 BAC 54, the top one, 00:13:50.21 is 140 kilobases in size. 00:13:53.18 And then these two orange fragments, above, 00:13:56.25 were also fragments 00:13:59.18 that we made into mice, 00:14:00.25 but they failed to rescue. 00:14:03.02 And so this told us that the gene 00:14:05.03 had to be located in this region 00:14:06.25 where the yellow bars are located, 00:14:09.21 and ultimately we found that 00:14:14.06 there was a very large gene 00:14:16.06 in this region shown here at the top, 00:14:18.20 which eventually we named the Clock gene. 00:14:22.09 Okay? 00:14:23.18 It's a very interesting gene. 00:14:24.28 It's about 100,000 basepairs in size, 00:14:27.16 it's very big, 00:14:29.04 and it has 24 exons. 00:14:31.24 We say, facetiously, 00:14:33.21 one exon for every hour of the day. 00:14:37.25 Now, in that mutant mouse, 00:14:39.27 there was only a single DNA change 00:14:45.12 between the mutant and the wild type, 00:14:47.16 and that was an A to T transversion, 00:14:49.27 shown here, 00:14:52.00 in a splice donor site, 00:14:54.24 that caused skipping of a single exon 00:14:57.09 in the Clock protein. 00:15:00.01 Now, the cloning of this gene 00:15:04.12 revealed that the Clock protein, 00:15:06.25 the predicted protein, 00:15:08.22 was very interesting, 00:15:10.07 because it had some clear 00:15:13.05 protein motifs indicating its function. 00:15:16.00 So, it had 00:15:18.05 a basic helix-loop-helix domain, 00:15:20.28 shown in green, 00:15:23.04 which is a DNA binding domain. 00:15:24.19 It then had a second domain called PAS, 00:15:29.22 which is a protein interaction domain. 00:15:32.20 Incredibly, that's the sequence 00:15:35.07 that was found in the original period protein. 00:15:38.15 And then, finally, in the C-terminus of the protein, 00:15:42.18 there's a glutamine, or Q-rich region, 00:15:46.15 which is characteristic 00:15:49.04 of activation domains of transcription factors, 00:15:51.06 or proteins that activate transcription. 00:15:55.02 So Clock turned out to be very interesting 00:15:58.07 because the amino acid sequence 00:16:00.06 gave us very strong indications 00:16:02.01 of what the function of this protein might be. 00:16:05.03 This is really not the case 00:16:06.29 for the period protein, 00:16:09.03 the original first clock protein to be found, 00:16:10.29 because period only had 00:16:13.05 this PAS domain, 00:16:14.17 which at the time 00:16:17.03 really had no known function. 00:16:21.16 So, what is CLOCK doing? 00:16:23.04 So, shortly after isolating CLOCK, 00:16:27.08 we found that it interacted 00:16:29.14 with a second protein called BMAL1, 00:16:32.03 in work that we did with 00:16:34.21 Charles Weitz at Harvard, 00:16:36.21 and it turned out that 00:16:39.10 BMAL and CLOCK 00:16:41.18 form what's called a transcriptional activator. 00:16:44.02 They bind to regulatory DNA sequences 00:16:47.07 in the promoters of genes. 00:16:49.22 One of those genes 00:16:51.17 turned out to be the period gene itself, 00:16:54.21 and it turns out the CLOCK protein 00:16:59.03 can still interact with BMAL, 00:17:01.23 can still bind DNA, 00:17:03.22 but its deficient in 00:17:06.15 activating transcription of many genes. 00:17:10.18 So, this animation 00:17:13.03 then shows you 00:17:15.09 how we think CLOCK, BMAL, 00:17:18.00 Period, and Cryptochrome 00:17:19.29 work together to form 00:17:22.01 a molecular clock in mammals. 00:17:23.22 So, there are three Period genes, 00:17:24.27 Per1, Per2, and Per3, 00:17:26.22 and two Cryptochrome genes, 00:17:28.15 Cry 1 and Cry2, 00:17:30.09 and these genes are both 00:17:32.09 activated by CLOCK and BMAL in the daytime. 00:17:35.04 Their RNAs are transcribed, 00:17:37.12 the proteins are translated in the cytoplasm. 00:17:41.21 The PER and CRY proteins, 00:17:43.04 as we call them for short, 00:17:45.08 can interact with each other 00:17:47.06 and then they translocate 00:17:49.08 back into the nucleus at night, 00:17:51.06 and as their levels increase at night 00:17:56.01 they then interact directly 00:17:57.29 with CLOCK and BMAL, 00:17:59.22 and repress 00:18:02.14 the activation potential of CLOCK/BMAL, 00:18:04.23 which thereby turns off their own transcription. 00:18:09.01 Once their transcription goes down, 00:18:10.19 their protein products go down, 00:18:13.13 and eventually at the end of the night 00:18:15.11 the PER and CRY proteins 00:18:17.17 are turned over and disappear, 00:18:19.14 and the next morning 00:18:21.16 CLOCK and BMAL can then 00:18:23.14 activate a new round of transcription. 00:18:25.18 So, this is a very simple description 00:18:28.10 of what we call the core feedback loop, 00:18:30.27 which makes up the mechanism 00:18:34.13 of the circadian clock in mammals. 00:18:37.21 So, incredibly, 00:18:42.11 work done by Louie Ptáček 00:18:45.12 showed that there was 00:18:48.15 a sleep disorder 00:18:50.21 that is caused by a change in the timing 00:18:52.18 of sleep preference in humans, 00:18:54.13 which is called Advanced Sleep Phase Syndrome, 00:18:57.13 and this is an ASPS pedigree 00:19:01.01 from Ptáček, 00:19:03.07 and when they found the causative gene 00:19:05.23 in this family, 00:19:07.10 what they found was 00:19:08.28 it was caused by a mutation 00:19:10.27 in the human Period2 gene, 00:19:13.29 one of the clock genes that we were just talking about. 00:19:15.29 So, this pathway that we described, 00:19:18.11 we now know is conserved 00:19:21.10 from mouse all the way to humans, 00:19:24.21 and distant relatives of these genes 00:19:27.04 also are conserved in fruit flies. 00:19:31.21 So, this is 00:19:35.00 a more modern view 00:19:36.27 of the clock gene network. 00:19:38.11 In the middle here is that 00:19:41.10 core transcriptional feedback loop, 00:19:43.10 with CLOCK, PER, and CRY 00:19:46.05 but, in addition, 00:19:49.09 at the top is a second loop 00:19:51.02 discovered in about 2002 00:19:52.16 by Ueli Schibler's lab 00:19:53.29 that involves the nuclear receptors 00:19:57.02 REV-ERBα and ROR. 00:20:00.08 These form a second feedback loop 00:20:03.16 and it turns out that they regulate 00:20:05.06 the transcription of BMAL in most tissues, 00:20:08.21 and CLOCK in some tissues, 00:20:11.01 to cause a second oscillation. 00:20:13.11 Also shown in this diagram 00:20:16.17 are two very important pathways 00:20:20.08 that regulate the stability 00:20:22.16 of the CRY protein and the PERIOD protein. 00:20:24.21 It turns out that the half-lives 00:20:27.13 of these two repressor proteins 00:20:29.24 are very importan 00:20:32.23 t in regulating the periodicity of the clock. 00:20:35.06 And then finally, at the bottom, 00:20:37.27 CLOCK and BMAL 00:20:40.27 turn out to have many other target genes 00:20:43.05 in the genome, 00:20:44.27 which we'll discuss later. 00:20:47.09 So, how is that we study the clock? 00:20:52.17 And so, one of the important tools that we use 00:20:55.17 is to use reporter genes 00:20:58.01 such as luciferase, or firefly luciferase, 00:21:02.18 an enzyme that can generate light from a substrate... 00:21:07.21 we use luciferase and 00:21:11.16 we fuse it to the PERIOD2 protein, 00:21:13.15 and then replace the PERIOD2 gene in a mouse 00:21:18.20 with a PERIOD2-luciferase fusion gene, 00:21:23.11 and what this allows us to do is to 00:21:27.01 visualize the amount of PER2 protein. 00:21:30.15 So, this time lapse movie shown here on the right 00:21:33.23 is a 7 day timelapse recording 00:21:38.01 of a brain slice that contains 00:21:40.13 the suprachiasmatic nucleus of a mouse, 00:21:43.18 in which we can see 00:21:46.08 luciferase levels oscillating in level, 00:21:51.20 but also spatially 00:21:54.09 we can see a pattern of luciferase 00:21:57.25 as it progresses as a wave across the nucleus. 00:22:04.04 Using this kind of reporter, 00:22:07.29 we can even go the cellular level. 00:22:11.02 So, this movie here shows a timelapse movie, 00:22:14.26 recorded by David Welsh, 00:22:16.24 for a six week period in time, 00:22:19.13 in which it's possible to follow 00:22:22.03 the circadian rhythm 00:22:24.00 of individual fibroblast cells. 00:22:26.24 So, in this movie, each of these twinkling stars 00:22:29.22 is a single cell 00:22:32.18 that is oscillating over a six period, 00:22:34.19 and then on the right here 00:22:36.09 I've given you six examples of individual cells 00:22:39.05 that are showing circadian oscillations 00:22:41.23 that persist for the duration of this experiment. 00:22:45.29 Now, this experiment 00:22:49.00 really changed our view completely 00:22:51.06 about how the clock works. 00:22:53.05 So, I told you there's a clock 00:22:55.17 in the hypothalamus, the suprachiasmatic nucleus, 00:22:57.25 and we've known about that clock since the 1970s, 00:23:01.08 but we really did not realize that 00:23:05.05 the body also contained such robust oscillators, 00:23:08.16 especially at the single cell level. 00:23:12.03 And it really wasn't until we could literally 00:23:14.01 see the single cell rhythm 00:23:15.26 that we could appreciate how robust or strong 00:23:19.24 the cell-autonomous clock is in our bodies. 00:23:26.09 So, we now know that 00:23:30.08 virtually all of our major organ systems in the body 00:23:34.05 contain circadian clocks 00:23:36.14 that are driven by these cell-autonomous clocks. 00:23:40.13 The brain SCN clock 00:23:42.18 we think is still in charge, 00:23:44.17 but many different tissues throughout the body 00:23:47.08 -- the liver, pancreas, lungs, 00:23:49.09 even skin -- 00:23:51.06 have the capability of generating circadian rhythms. 00:23:54.25 And this has really led to 00:23:57.08 a whole new set of questions 00:23:59.10 having to do with, 00:24:00.25 what is the function of these clocks in the body, 00:24:04.05 and how might they be controlled in the organism? 00:24:09.18 So, one example of 00:24:12.11 what we would call a peripheral clock 00:24:14.27 are clocks in the pancreas. 00:24:17.00 So, these experiments, 00:24:18.25 done by my colleague at Northwestern, Joe Bass, 00:24:21.18 shows that pancreatic islet cells 00:24:24.26 have beautiful circadian rhythms, 00:24:26.29 as you can see here with PER::LUC recordings. 00:24:31.17 And we then asked what happens 00:24:35.04 if we disrupt the gene for the clock, 00:24:38.10 in this case the BMAL1 gene, 00:24:40.28 specifically in pancreatic beta cells. 00:24:44.21 And this shows 00:24:47.20 two photomicrographs of pancreatic islets. 00:24:50.23 The red indicates BMAL protein, 00:24:54.09 which has been deleted 00:24:57.05 in the BMAL pancreatic-specific knockout, 00:24:58.28 shown here. 00:25:00.05 The green and blue 00:25:02.28 show insulin and 00:25:06.13 the rest of the islet cell anatomy, here. 00:25:09.09 What we find is 00:25:12.02 by deleting BMAL, 00:25:15.08 the mouse has a loss of glucose regulation, 00:25:18.06 as shown here, 00:25:20.12 compared to control mice that have intact BMAL 00:25:23.18 in the pancreas. 00:25:25.04 And this loss of glucose regulation 00:25:28.01 is due to insufficient insulin production, 00:25:32.24 as shown in this bottom graph here. 00:25:36.03 So this is a very nice example 00:25:38.09 of the function of a clock gene 00:25:40.01 in a peripheral tissue, 00:25:42.01 which can lead to diabetes 00:25:46.08 and insufficient insulin production. 00:25:56.17 So, how is it that timing information 00:25:59.20 might be integrated in the SCN 00:26:02.26 and throughout the whole body? 00:26:04.25 So, if we do a thought experiment, 00:26:08.17 this is actually an experiment 00:26:11.25 posed by the philosopher Derek Parfit at Oxford, 00:26:16.04 in which he had a scientist 00:26:20.11 gradually replace the cells in his body 00:26:23.29 with the cells from Greta Garbo, 00:26:26.21 and he asked, 00:26:29.27 as you increase the number of cells 00:26:34.06 that were replaced in your body, 00:26:36.01 at what time during that process 00:26:38.22 do you cease to be yourself 00:26:40.29 and become Greta Garbo? 00:26:44.20 Now, for those of you 00:26:47.25 who aren't familiar with Greta Garbo, of course, 00:26:49.23 you might just think of Angelina Jolie 00:26:53.25 as a substitute or a modern day version 00:26:55.26 of Greta Garbo. 00:26:57.13 Now, interestingly, 00:26:59.07 that very provocative kind of experiment 00:27:01.29 can be done in the laboratory - 00:27:05.17 we can do that in mice. 00:27:08.22 So, the way we do that is that we can 00:27:11.08 mix the cells 00:27:14.11 from two different strains of mice together 00:27:16.14 and create a single mouse 00:27:19.00 that contains cells derived 00:27:21.10 from two different types of mice. 00:27:23.20 And these mice are called chimeras. 00:27:26.17 And so the way these mice are made 00:27:28.09 is you prepare embryos 00:27:31.17 from the two strains of mice, 00:27:33.13 indicated in blue and yellow, 00:27:35.20 you can mix these embryos 00:27:37.14 at an early stage, 00:27:39.10 and they will form a chimeric embryo, 00:27:43.01 which you can then transplant into mice 00:27:46.01 and then produce chimeric mice. 00:27:48.19 So, this kind of experiment 00:27:50.16 was done by Sharon Low-Zeddies. 00:27:52.29 She made the mice, 00:27:55.07 tested their circadian behavior, 00:27:58.23 and then, using anatomical methods, 00:28:03.09 looked at the proportion and distribution 00:28:05.25 of those cells in the brain. 00:28:08.09 So, in this case, 00:28:09.27 the normal cells were marked by the LacZ gene, 00:28:14.05 which we could visualize with blue dye. 00:28:17.00 So, this slide shows 00:28:19.00 the two strains of mice we used. 00:28:20.12 On the left are the wild type strain, 00:28:22.22 which is pigmented, 00:28:24.00 has normal behavior, 00:28:25.15 and is LacZ positive, 00:28:27.05 so the cells are blue. 00:28:29.17 And on the right is the mutant strain, 00:28:31.25 which is albino, 00:28:33.14 has Clock mutant behavior, 00:28:35.26 and the cells are LacZ negative. 00:28:38.16 And in the middle is shown 00:28:40.26 the genetic cross of these two strains, 00:28:43.08 the F1, 00:28:45.15 which confirms that the behavior 00:28:48.11 is intermediate in phenotype. 00:28:51.02 Sharon made a huge number of chimeras, 00:28:54.22 these are a set of them, 00:28:58.06 and what you can see already is that 00:29:01.06 mice at the top are carrying pigmentation 00:29:02.28 in their coat colors 00:29:04.27 and the mice at the bottom 00:29:06.22 are generally albino, 00:29:08.14 and that's because they were ordered by their phenotype. 00:29:11.16 If we look at their SCNs, 00:29:13.25 you can also see that the mice at the top 00:29:16.27 have more blue cells, 00:29:18.20 those are wild type cells, 00:29:21.03 and the mice at the bottom have more mutant cells, 00:29:23.08 which are white. 00:29:25.12 Okay? 00:29:26.28 And then, finally, 00:29:29.05 this illustration here 00:29:31.07 shows you the activity record 00:29:32.29 of each one of these mice 00:29:34.16 as little postage stamps, 00:29:36.22 but what's important... 00:29:38.07 it's a little hard to see but what's important is that 00:29:41.06 at the top the mice have wild type 00:29:43.21 or normal behavior, 00:29:45.15 at the bottom they have Clock mutant behavior, 00:29:50.00 but in the middle they show something very interesting. 00:29:54.16 They show an intermediate phenotype. 00:29:58.26 And so here's a better picture of this idea. 00:30:04.03 The left shows the mice 00:30:06.08 that have mainly blue or wild type cells, 00:30:09.11 and then in the middle are mice 00:30:11.23 that are carrying about a 50-50 mixture 00:30:14.01 of mutant and normal cells, 00:30:17.18 and then here are Clock mutants, 00:30:19.14 which are predominantly blue-negative, 00:30:22.15 which have mutant behavior. 00:30:25.21 What's interesting is that the behavior 00:30:28.00 is intermediate 00:30:30.18 when we have a 50-50 mixture of cells. 00:30:34.01 Okay? 00:30:35.12 This is shown, here, 00:30:38.01 for three examples 00:30:40.29 of mice carrying about 50-50 mixtures of cells, 00:30:45.03 and what is really amazing here is that 00:30:47.29 these mice have behavior 00:30:50.15 that's identical to a Clock heterozygous mouse, 00:30:55.10 but no cell in the body of those mice 00:30:58.29 is Clock heterozygous. 00:31:01.20 They're either homozygous Clock 00:31:04.11 or wildtype. 00:31:06.16 It's a mixture. 00:31:08.05 So we call them Clock heterozygous phenocopies 00:31:11.15 because they have the same behavior, 00:31:14.17 but genetically they're not the same. 00:31:18.06 Okay? 00:31:19.17 And what this tells us is that 00:31:22.10 the system that controls circadian behavior in the mouse 00:31:26.07 has some way of integrating information 00:31:30.16 across wild type and mutant cells 00:31:33.29 to, in some kind of additive way, 00:31:37.12 produce an intermediate phenotype 00:31:40.18 that actually is equivalent to what you would see 00:31:43.14 in a genetically intermediate cell. 00:31:46.21 So this kind of experiment 00:31:49.15 shows the circadian system really is 00:31:52.05 integrating quantitative information 00:31:56.19 in a very additive 00:31:59.17 or almost mathematical way 00:32:02.11 to generate behavior in the mouse. 00:32:05.13 So, in summary, 00:32:09.00 I've tried to summarize for you, really, 00:32:14.07 the discovery of the clock gene network, 00:32:17.01 and then how we study those genes 00:32:19.19 in a cellular context, 00:32:23.03 both at the cell-autonomous level 00:32:25.19 and at the organismal level, 00:32:27.26 looking at tissue organization. 00:32:30.06 And so what we can see from this work 00:32:32.10 is that clock cells 00:32:36.25 play a very important role throughout the body, 00:32:38.29 but how they're organized 00:32:40.28 is fundamentally different in the brain 00:32:43.19 and the periphery, 00:32:45.14 and in the next section we'll go into 00:32:49.13 the actual details of how those differences occur.

Part 2: Clock Genes, Clock Cells and Clock Circuits Continued

00:00:07.28 So, in this second section, 00:00:10.10 what I'd like to do is to really 00:00:12.21 look in more detail 00:00:14.15 at the differences between 00:00:16.26 central and peripheral oscillators 00:00:20.16 using both genetic and non-genetic methods 00:00:25.17 of perturbing the circadian system. 00:00:28.02 So, one way that we have looked at this 00:00:31.22 is to go back and examine 00:00:34.25 some of what we would call 00:00:36.19 the classic mutants 00:00:39.05 of either Period or Cryptochrome, 00:00:42.09 which are shown here 00:00:44.11 for Cryptochrome 1 and 2. 00:00:45.29 These are loss of function or knockout mice, 00:00:49.04 and in this case what we found 00:00:51.20 is that if you delete Cry1, 00:00:54.11 the mouse still has a rhythm, 00:00:56.14 but it's one hour short. 00:00:58.22 If you delete Cry2, 00:01:00.23 the mouse still has a rhythm, 00:01:03.20 but in this case it's long. 00:01:08.12 And then if you delete both genes, 00:01:10.15 Cry1 and Cry2, 00:01:12.15 the mouse then loses its rhythm, 00:01:14.06 and this is really the reason that we called 00:01:19.02 Cry1 and Cry2 00:01:21.09 part of the central clock gene network. 00:01:23.06 And so Cry1 and 2 mice 00:01:26.10 have no rhythm; they're arrhythmic. 00:01:28.25 And so what we've done is to then ask, 00:01:31.15 what are the effects of these mutations, 00:01:33.22 such as Cry1 and 2, 00:01:35.16 on the SCN clock and a peripheral clock, 00:01:40.16 in this case this example shows the lung. 00:01:44.02 And so this is using this PER::LUC imaging 00:01:48.10 in a wild type mouse 00:01:51.00 for the SCN and for lung, 00:01:53.23 and what you can see is 00:01:58.00 both tissues have very nice rhythms of PER::LUCIFERASE, 00:02:01.08 but if we knock out either Per1 or Cry1, 00:02:06.06 this leads to a strong reduction 00:02:10.06 in the rhythm in the lung, 00:02:12.28 but has very little effect 00:02:15.08 in the suprachiasmatic nucleus. 00:02:17.23 In the suprachiasmatic nucleus, 00:02:20.03 we have to do the double knockout, 00:02:21.14 as we did for behavior for Cry1 and Cry2. 00:02:24.14 This of course works in the lung as well, 00:02:27.21 but in peripheral tissues 00:02:30.26 we see a clear difference. 00:02:33.19 It's not just any Cry gene 00:02:35.20 that has this effect, 00:02:37.04 so for example Cry1 00:02:39.12 leads to this loss of rhythm phenotype, 00:02:41.05 shown here, 00:02:42.17 but Cry2 doesn't. 00:02:44.08 The same is true for Per1 and Per3. 00:02:47.02 So, there is clearly some difference 00:02:49.19 in the Per and Cry genes, 00:02:51.15 and some specificity in their role in the clock system. 00:02:56.24 So, to look into this further, 00:03:00.05 we then asked, 00:03:02.16 what effect do these mutations have 00:03:04.25 on single-cell rhythm? 00:03:07.19 So these are now single-cell recordings 00:03:09.29 from either fibroblasts or 00:03:14.18 dissociated, isolated SCN neurons. 00:03:17.10 Okay? 00:03:19.02 And what we find is a very interesting result, 00:03:21.15 and that is that the gene mutations Cry1 and Per1 00:03:26.10 have the same effect in a fibroblast 00:03:30.08 as they do in the SCN neuron, 00:03:33.13 and this is surprising because we thought before 00:03:36.05 that perhaps the SCN might be different, 00:03:38.09 it might be more robust. 00:03:40.17 And as you remember, 00:03:42.09 in the previous slide 00:03:44.11 I showed you that the SCN 00:03:46.04 was resistant to these mutations, 00:03:47.28 but that's because 00:03:50.14 in that experiment the SCN itself 00:03:54.07 was somewhat intact, 00:03:56.16 it was in an organotypic slice, 00:04:00.04 where the organization of the SCN it still intact, 00:04:04.04 as compared to 00:04:06.27 physically dissociated SCN neurons. 00:04:08.13 So, here's an experiment 00:04:12.16 in which the SCN in a slice 00:04:17.03 is compared to SCN dissociated neurons, 00:04:21.00 looking at the effect of the Cry2 knockout. 00:04:24.10 So, on the bottom are shown 00:04:26.23 heat map representations 00:04:28.12 of single-cell recordings from SCN neurons, 00:04:32.29 about 20 cells in each case, 00:04:34.28 and what you can see is in Cry2 knockout SCN neurons, 00:04:40.09 the cells are coherent and synchronized, 00:04:45.18 as indicated by the red and green stripes, 00:04:50.15 but in dissociated SCN neurons, 00:04:52.23 each of the cells can generate 00:04:54.22 intact circadian rhythms, 00:04:56.17 but they are no longer coupled, 00:04:58.16 and so the pattern becomes fragmented. 00:05:02.06 In contrast, in Cry1 knockout SCN neurons, 00:05:07.12 we see that in the intact SCN, 00:05:09.24 rhythms are generated and are coherent, 00:05:14.07 but when we dissociate the cells 00:05:16.17 the SCN cells can no longer 00:05:19.05 generate strong circadian rhythms, 00:05:21.14 and at the cell-autonomous level 00:05:23.13 the rhythms are disrupted. 00:05:25.13 So, these genetic experiments 00:05:27.20 have really uncovered 00:05:30.07 a new role for the suprachiasmatic nucleus, 00:05:35.28 and that is to be able to integrate the information 00:05:39.21 from many cells. 00:05:40.26 And so what we saw in these genetic experiments 00:05:45.25 is that the Cry1 mutation 00:05:48.14 could actually lead to a loss of rhythm 00:05:50.23 in the cell-autonomous level, 00:05:53.19 which was then reflected in peripheral tissues, 00:05:58.04 but in contrast the Cry2 neurons, 00:06:02.17 which have intact rhythms, 00:06:05.04 then did not have any effect 00:06:08.14 on peripheral tissues. 00:06:10.20 In contrast, in suprachiasmatic nucleus tissue, 00:06:14.18 we found a very interesting result, 00:06:17.10 where the cell-autonomous defect 00:06:20.07 can actually be rescued 00:06:22.25 by the SCN network. 00:06:24.10 Interestingly, because the SCN 00:06:26.29 then regulates circadian behavior, 00:06:29.26 we can see that at the behavioral level 00:06:33.21 the Cry1 mutant is also rescued. 00:06:38.27 And so I think these experiments 00:06:40.21 are important for a number of reasons. 00:06:42.07 One is that it shows that 00:06:47.00 circadian behavior is really 00:06:50.14 not a direct reflection of the cell-autonomous oscillator; 00:06:55.26 information at the cell-autonomous level 00:06:57.18 can be transformed by the SCN network 00:07:01.20 to rescue that function, 00:07:04.22 which then in turn rescues circadian behavior. 00:07:08.17 On the other hand, 00:07:10.09 at another level, 00:07:11.21 if we were interested in the specific role 00:07:14.01 of, say, Cry1 or Cry2, 00:07:15.29 then trying to interpret 00:07:19.11 the role of Cry1 and Cry2 00:07:21.07 purely on the basis of behavior 00:07:23.19 might be misleading, 00:07:25.20 because we see this very 00:07:28.12 different cell-autonomous defect 00:07:30.16 at the level of Cry1 and Cry2. 00:07:33.07 And so if we're trying to understand 00:07:35.26 the biochemical function of Cry1, 00:07:38.08 then it might make more sense, for example, 00:07:43.00 to study the cell-autonomous clock, 00:07:45.08 rather than the SCN or behavioral clock. 00:07:50.26 So, going back to the organization 00:07:53.13 of circadian rhythms, 00:07:55.17 how is it that rhythms 00:07:58.16 are really synchronized and orchestrated 00:08:01.21 throughout the entire organism? 00:08:04.26 So, we know that the SCN 00:08:07.01 is really still in charge. 00:08:08.19 So for example, 00:08:10.01 in these experiments shown on the left... 00:08:12.10 these are records of control mice, 00:08:15.00 and then at the bottom 00:08:16.29 are records of SCN lesion mice. 00:08:19.26 What SCN lesion does 00:08:21.29 is to disrupt the behavioral rhythm, 00:08:25.17 and with PER::LUC recording of peripheral tissues, 00:08:28.29 we can then ask, 00:08:30.24 what is the effect of SCN lesioning 00:08:33.08 of the central clock 00:08:35.00 on peripheral rhythms? 00:08:36.26 And so shown here 00:08:40.27 are PER::LUCIFERASE tracings from the pituitary, 00:08:43.19 a peripheral oscillator, 00:08:46.28 and in intact mice 00:08:51.06 the pituitary gland rhythms 00:08:53.03 are actually very normal 00:08:56.11 in either light-dark cycles or in constant darkness. 00:09:01.06 But when we lesion the suprachiasmatic nucleus, 00:09:03.28 what we find is that 00:09:07.22 peripheral tissues become desynchronized, 00:09:11.14 so when we compare the peripheral rhythms 00:09:13.25 from different mice, 00:09:15.10 we see that they have adopted different phases. 00:09:18.07 Each mouse has a slightly different phase 00:09:21.22 for its pituitary and other peripheral tissues. 00:09:27.00 So, interestingly, 00:09:29.12 the SCN is not necessary for maintaining rhythms 00:09:32.23 in peripheral tissues, 00:09:34.12 but it plays a role 00:09:36.28 in synchronizing or coordinating those rhythms. 00:09:41.01 So, how is it that the SCN 00:09:44.14 really communicates this information? 00:09:47.20 So, we know that light 00:09:49.07 is one of the major 00:09:51.15 inputs to the brain and the SCN, 00:09:54.05 which then controls many behaviors, 00:09:56.18 such as feeding and sleep-wake cycles, 00:09:59.07 but recent work has also shown 00:10:01.24 a very important role for 00:10:05.08 nutritional cycles and signals, 00:10:07.08 as well as feeding behavior, 00:10:10.03 particularly for regulating peripheral tissues 00:10:14.16 such as the liver. 00:10:18.27 Now, to really address this, 00:10:22.05 we've gone back and examined 00:10:24.14 a second environmental signal, 00:10:26.09 and that is temperature. 00:10:28.08 So, in almost every organism 00:10:32.06 living in the free world, 00:10:34.26 light and temperature both synchronize clocks, 00:10:39.20 and temperature 00:10:42.22 is involved both in entrainment, 00:10:44.19 or synchronization of rhythms, 00:10:46.00 but there's also an interesting feature of rhythms 00:10:50.03 called temperature compensation, 00:10:51.23 and that is that the period of the rhythm 00:10:54.15 is resistant to dramatic changes in temperature, 00:10:59.06 so the period is actually compensated 00:11:02.07 against temperature fluctuations. 00:11:06.24 Now, mammals are actually a little bit unusual. 00:11:09.12 So, this is a record of a mouse, 00:11:11.16 it's a very long activity record, 00:11:14.20 and at the top the mouse 00:11:17.18 is in a constant temperature, 00:11:19.02 but it's exposed to a light cycle 00:11:20.13 which synchronizes its rhythm, shown here. 00:11:23.06 It goes into darkness at this point 00:11:25.02 and then, at the bottom of this record, 00:11:27.15 shown in the gray bar, 00:11:29.24 is a temperature cycle 00:11:32.20 of about 24-32°C, 00:11:37.06 and what you can see is that 00:11:39.15 this temperature cycle 00:11:42.12 can synchronize the rhythm transiently, 00:11:45.13 but it's not very strong, 00:11:47.01 so over time the activity rhythm 00:11:50.29 breaks away and free runs. 00:11:52.14 So, in mammals, temperature is 00:11:56.12 kind of a weak entraining signal 00:11:58.19 for circadian rhythms 00:12:00.26 at the whole-organismal level. 00:12:03.01 But interestingly, mice, as in humans, 00:12:07.14 have a very dramatic circadian body temperature rhythm, 00:12:10.23 and so this is a temperature recording 00:12:13.14 from a mouse over a ten day period, 00:12:16.16 and what you can see is the body temperature 00:12:19.02 fluctuates from about 36°C 00:12:21.18 at the lowest 00:12:23.08 to about 38.5°C at the peak, 00:12:25.25 each day. 00:12:27.25 And so Ethan Buhr asked, 00:12:31.13 can this subtle change in temperature, 2.5°C, 00:12:36.22 actually perturb or entrain 00:12:39.16 the phase of clocks in the periphery? 00:12:42.07 So, this is a PER::LUC recording 00:12:45.11 from liver tissue samples, 00:12:48.07 and at this point they were given 00:12:52.03 a temperature pulse of just 2.5°C 00:12:55.07 for six hours to the liver, 00:12:58.09 shown in the red trace, 00:13:00.09 and in the blue trace is another liver sample 00:13:04.00 that was handled the same, 00:13:05.26 but did not receive the temperature change, 00:13:08.24 and what you can see is, after this treatment, 00:13:12.07 the liver exposed to this temperature pulse 00:13:15.26 is delayed. 00:13:17.24 The phase is changed. 00:13:20.10 And if we do this experiment systematically, 00:13:23.04 we give a temperature pulse 00:13:26.05 at all times of the cycle, 00:13:28.02 shown on the x-axis of this graph... 00:13:30.26 this is a graph called a phase transition curve, 00:13:33.27 it plots the phase of the rhythm on the x-axis 00:13:38.26 and then the new phase of the rhythm on the y-axis. 00:13:43.01 Okay? 00:13:45.15 So, if you were to give 00:13:50.22 a stimulus that had no effect, 00:13:53.08 then the old phase and the new phase 00:13:56.07 would be the same, 00:13:58.19 and all the data points would lie on this 45° line, 00:14:03.20 where the blue points are. 00:14:05.06 Those are the handling controls. 00:14:07.26 You can see that they have no effect. 00:14:10.10 But temperature has a very strong resetting effect, 00:14:13.17 those data are shown in red dots. 00:14:15.26 They reset at almost any time of day 00:14:20.03 to a new set of phases, 00:14:22.27 okay? 00:14:24.00 And these data have a horizontal slope, 00:14:29.04 a slope of 0. 00:14:31.02 This is called strong resetting. 00:14:34.02 It's also called type 0 resetting, 00:14:36.08 because the slope is 0, 00:14:38.04 as opposed to type 1 resetting, 00:14:40.06 a slope of 1, 00:14:41.16 which is weak resetting. 00:14:43.10 So, temperature turns out to be 00:14:46.00 a very strong signal to peripheral clocks 00:14:49.28 such as those found in the liver. 00:14:53.04 And so, this is another set of experiments, 00:14:56.26 in this case, the pituitary gland. 00:15:00.11 The blue and red dots now 00:15:02.17 indicate different duration temperature pulses. 00:15:05.12 The blue dots are 1 hour temperature pulses 00:15:08.08 and the red dots are six hour temperature pulses, 00:15:11.01 as we saw before. 00:15:12.25 And as we can see here, 00:15:15.05 the pituitary shows strong resetting; 00:15:18.15 the slope of these data are 0. 00:15:21.03 Okay? 00:15:22.08 But surprisingly, 00:15:24.06 when we look at the suprachiasmatic nucleus 00:15:26.29 in the same kind of conditions, 00:15:30.04 those data are all type 1, 00:15:34.04 or very weak resetting, 00:15:36.07 so the SCN is resistant 00:15:38.26 to temperature resetting pulses. 00:15:43.25 So, we then asked, 00:15:48.09 can the body temperature profile in a mouse 00:15:51.08 act to synchronize rhythms in peripheral tissues? 00:15:55.00 So, this shows you the average profile 00:15:58.25 measured from a mouse over one day, 00:16:03.28 and what Ethan Buhr then did 00:16:06.15 was to program this temperature profile 00:16:09.15 into an incubator 00:16:13.21 and expose different peripheral tissues 00:16:16.03 to these cycles. 00:16:17.15 So, the blue cycles 00:16:19.21 indicate one phase 00:16:21.18 and the red cycles indicate 00:16:23.20 a temperature cycle that's shifted 00:16:25.22 to the opposite phase. 00:16:27.27 And in these two examples shown here, 00:16:29.23 these are pituitary glands 00:16:31.25 that were exposed to three cycles 00:16:33.19 at these temperature cycles. 00:16:35.24 The red trace indicates 00:16:38.07 the phase of the pituitary rhythm 00:16:40.08 exposed to the red temperature cycles, 00:16:43.00 and the blue trace 00:16:44.29 indicates the phase of the rhythm 00:16:47.26 in a pituitary gland exposed 00:16:50.08 to the blue temperature cycles. 00:16:51.10 And what you can see is 00:16:53.29 the two sets of pituitaries 00:16:56.17 are out of phase, 00:16:58.20 and they match the phase of the temperature cycle. 00:17:01.03 That means that the temperature cycle 00:17:03.20 reset the phase, 00:17:05.25 within three days, 00:17:07.26 of both the pituitary gland and the lung, 00:17:10.24 in this case at that bottom. 00:17:13.01 So, the very subtle body temperature variation 00:17:16.19 in the mouse 00:17:18.13 is a very strong signal 00:17:20.07 and can completely reset the oscillators 00:17:23.06 in different organs. 00:17:25.07 Okay. 00:17:26.22 So, what I'd like to do now is 00:17:29.02 to go back to the SCN and ask, 00:17:31.09 why is it that the SCN 00:17:33.16 is different from a peripheral tissue? 00:17:36.24 Why is it resistant to temperature? 00:17:40.01 And as we saw in the case of 00:17:42.00 those genetic experiment before, 00:17:43.14 coupling in the SCN 00:17:46.13 might be an important factor. 00:17:48.25 And so we can use a drug 00:17:51.27 called tetrodotoxin, or TTX, 00:17:54.21 which blocks sodium-dependent action potentials 00:17:58.12 in the suprachiasmatic nucleus, 00:18:00.26 and can uncouple or desynchronize 00:18:03.21 the neurons in the SCN. 00:18:05.17 So, this panel on the left 00:18:08.10 shows single cell recordings of SCN neurons, 00:18:12.14 indicated in [red/green] heatmaps, 00:18:16.15 which were treated with tetrodotoxin, 00:18:18.28 and what happens is, at the single cell level, 00:18:21.09 those neurons start desynchronizing. 00:18:24.12 And when we give a temperature pulse, 00:18:26.13 incredibly, 00:18:28.22 now the SCN becomes sensitive to temperature. 00:18:31.11 So, at the top this is showing 00:18:34.18 SCN slices not treated with tetrodotoxin 00:18:37.21 -- they're resistant, they have type 1 resetting -- 00:18:40.22 and at the bottom 00:18:43.01 are SCN slices treated with tetrodotoxin. 00:18:46.05 Just that single manipulation alone 00:18:48.13 then converts the temperature sensitivity 00:18:51.02 to type 0 resetting, or very strong resetting, 00:18:55.01 just like a peripheral tissue. 00:18:57.08 So, this suggests that it is really the coupling 00:19:00.09 within the SCN 00:19:02.15 that is making it more robust 00:19:04.11 and more resistant to temperature resetting, 00:19:06.22 and also making it different from a peripheral tissue. 00:19:12.23 Now, interestingly, 00:19:14.09 the SCN has two major subdivisions. 00:19:17.19 One is called the ventrolateral or VL 00:19:20.01 and the other is called dorsomedial (DM), 00:19:23.10 and you can do a very simple experiment 00:19:26.01 and transect the SCN 00:19:29.12 to separate the dorsal and ventral regions of the nucleus, 00:19:33.29 as shown here. 00:19:35.12 When you culture those two 00:19:38.05 halves of the SCN, they both have rhythms, 00:19:41.12 but incredibly they now have strong, 00:19:45.01 or type 0 resetting. 00:19:47.07 In contrast, if we were to cut the SCN 00:19:50.15 down the midline, 00:19:52.26 both the right and the left SCN, of course, 00:19:54.23 still have rhythms, 00:19:56.22 but in this case they remain robust, 00:20:00.29 or resistant to temperature. 00:20:03.22 So, this very simple experiment 00:20:05.21 suggests that there's a pathway 00:20:07.28 between the ventrolateral and dorsomedial SCN 00:20:11.05 that confers this kind of temperature resistance, 00:20:15.24 again suggesting that coupling 00:20:18.04 is actually important within the nucleus 00:20:21.02 to make it robust. 00:20:24.15 So, what is it that senses temperature? 00:20:27.20 And so, in experiments from Ueli Schibler's lab, 00:20:32.13 where they screened 00:20:35.07 different transcription factors in the liver 00:20:37.19 for circadian expression patterns, 00:20:40.09 one of the most robust transcription factors that they found 00:20:44.17 was HSF1. 00:20:46.15 So, this is a western blot 00:20:48.19 showing the amount of HSF protein 00:20:53.16 in the nucleus of liver cells 00:20:56.06 over the time of day, 00:20:57.20 and what you can see is that in the daytime 00:21:00.08 there's virtually no HSF in the nucleus, 00:21:02.29 and then at night HSF1 is very abundant, 00:21:06.25 so this leads to a very strong pattern of HSF1 00:21:10.21 in the nucleus of liver cells. 00:21:16.28 And so, to test 00:21:20.04 whether HSF1 might be involved 00:21:22.20 in temperature sensing for resetting the clock, 00:21:27.25 we used an inhibitor of HSF1 called KNK437. 00:21:34.01 This inhibitor can very strongly 00:21:37.01 block the heatshock response in cells. 00:21:40.23 This is the HSP72 response to temperature. 00:21:44.17 In the presence of drug, 00:21:46.20 this is very strongly blocked. 00:21:49.11 And when we apply this inhibitor for HSF1 00:21:53.15 to different peripheral tissues, 00:21:55.12 such as the lung, 00:21:57.11 as a pulse for one hour, 00:22:00.04 we find that it causes 00:22:02.04 very strong resetting of the clock, 00:22:05.26 but interestingly the phase of that resetting curve 00:22:09.21 is slightly different from what we saw with temperature. 00:22:12.26 So, in the gray 00:22:16.11 are shown the temperature pulses 00:22:18.17 that we saw before for temperature increases. 00:22:23.01 In light blue are shown 00:22:26.16 resetting curves for "cool" pulses, 00:22:30.21 a reduction in temperature. 00:22:33.23 This also shifts the clock very effectively 00:22:35.25 and, interestingly, 00:22:37.19 KNK and cool pulses 00:22:40.07 have the same kind of effect on the clock. 00:22:43.07 So this suggests that inhibition of HSF1 00:22:46.23 mimics a temperature reduction, 00:22:49.00 and this is consistent with the idea, 00:22:51.17 because temperature normally increases HSF1. 00:22:55.12 A lowering of temperature would reduce HSF1, 00:22:59.18 as would inhibition of HSF1. 00:23:02.09 And so we think that this is evidence 00:23:07.01 that HSF1, in part, 00:23:08.27 can mediate the effects of both 00:23:11.06 cool and warm pulses 00:23:14.06 in resetting peripheral tissues. 00:23:16.11 Now, does HSF1 00:23:20.05 mediate temperature pulses? 00:23:21.16 And we can ask that question 00:23:23.13 by doing a blocking experiment. 00:23:25.00 We can ask, if we block 00:23:27.07 the increase in HSF1 with KNK437, 00:23:31.08 will this block the temperature shift, 00:23:34.02 and so this is an experiment shown on the top here. 00:23:37.08 The gray bar shows the effect of temperature 00:23:40.21 using a vehicle control, 00:23:42.25 so temperature is giving a very large reset. 00:23:46.19 At this same phase, 00:23:48.23 we can give the drug alone, 00:23:50.05 it causes no shift at this phase, 00:23:52.19 and then the third condition 00:23:55.05 is the drug plus the temperature pulse, 00:23:56.17 and you can see that there's no shift, 00:23:59.01 showing that KNK can completely block 00:24:02.00 temperature resetting. 00:24:03.12 So this is very strong evidence that 00:24:05.23 HSF1 elevations 00:24:08.20 are required for temperature resetting 00:24:10.10 in peripheral tissues. 00:24:13.14 And we can also do this experiment 00:24:15.20 in a more complex manner 00:24:17.06 by testing all phases of the cycle, 00:24:19.16 and that's shown in these resetting curves. 00:24:23.14 And what's important to see in these curves 00:24:25.22 is the gray dots 00:24:27.26 show the effect of temperature by itself, 00:24:29.21 and then the orange and red dots 00:24:31.22 show the effect of either drug, 00:24:33.29 or drug plus temperature, 00:24:35.10 which are indistinguishable. 00:24:37.06 And this shows the drug is 00:24:40.03 blocking the effect of temperature 00:24:41.22 at all phases of the cycle. 00:24:44.00 This is of course in a peripheral tissue. 00:24:47.20 And then finally, 00:24:49.26 interestingly, the SCN, 00:24:51.13 which was resistant to temperature, 00:24:54.14 is also resistant to the inhibitor of HSF1, KNK 00:24:59.17 -- it has a type 1 resetting curve 00:25:02.04 to the drug -- 00:25:04.01 further indicating that 00:25:07.13 this drug is working on the same pathway, 00:25:10.02 and that the SCN coupling network 00:25:13.02 can interfere with not only temperature pulses, 00:25:16.11 but also HSF1 interference. 00:25:21.27 Finally, the other feature of temperature 00:25:25.12 was this phenomenon that I mentioned before, 00:25:29.09 which is called temperature compensation. 00:25:33.07 And so this is an illustration 00:25:35.12 of temperature compensation in the SCN 00:25:37.06 and in the pituitary. 00:25:39.22 If you measure the period length 00:25:43.01 of the rhythm, shown here, 00:25:45.13 at different temperatures, 00:25:47.18 what we see is the period 00:25:50.20 is very similar. 00:25:51.25 And when we calculate the temperature coefficient, 00:25:54.14 or Q10, 00:25:56.03 we see that that coefficient is very close to 1 00:25:58.25 -- 0.97 in the case of pituitary 00:26:02.07 and 1.04 in the case of the SCN -- 00:26:05.18 almost perfect temperature compensation. 00:26:09.10 But if we expose these tissues 00:26:12.14 to the HSF1 inhibitor, KNK437, 00:26:16.10 we see that the Q10s now 00:26:20.08 are taken out of the circadian range 00:26:21.23 and become much bigger, 00:26:23.16 and you can see the orange curves here 00:26:25.21 are kind of slanted. 00:26:28.03 Finally, in blue, in the SCN, 00:26:31.22 we can ask, 00:26:33.23 what is the effect of treatment 00:26:37.16 with tetrodotoxin 00:26:39.22 and uncoupling the network? 00:26:42.06 And what we find is that 00:26:44.18 the Q10 is still the same, 1.06. 00:26:47.28 So, this is a very interesting difference. 00:26:49.20 Temperature compensation of period 00:26:51.27 does not depend on the SCN network. 00:26:54.27 It is a cell-autonomous property, 00:26:57.02 not only of SCN cells, 00:26:59.28 but the pituitary, peripheral tissues, and fibroblasts. 00:27:03.09 But temperature resistance 00:27:06.22 is a network phenomenon 00:27:09.08 that's characteristic of the SCN 00:27:11.16 and not peripheral tissues. 00:27:15.03 Okay. 00:27:17.10 So this is kind of an overall summary 00:27:19.23 of our understanding 00:27:21.27 of the role of temperature 00:27:24.18 as a signal for resetting peripheral clocks. 00:27:28.00 The suprachiasmatic nucleus 00:27:30.08 generates a circadian rhythm of body temperature, 00:27:34.28 this signal is propagated throughout the organism, 00:27:39.23 and can be used by 00:27:41.25 many different peripheral clocks, 00:27:44.00 and we believe that in these peripheral clocks 00:27:46.29 HSF1 is one of the signaling pathways 00:27:50.25 for mediating this temperature information 00:27:53.05 to reset those clocks. 00:27:56.04 Now, the SCN itself 00:27:58.22 is resistant to this body temperature signal 00:28:02.15 and in retrospect that kind of makes sense. 00:28:05.01 If the SCN is setting out a resetting signal, 00:28:10.01 then it might not be a good idea 00:28:12.15 for it to be sensitive 00:28:14.01 to its own resetting signal. 00:28:15.12 That might cause some kind of feedback problems. 00:28:19.01 And so we think that that could be the reason, 00:28:22.06 or one of the reasons, 00:28:24.12 that the SCN is really resistant to temperature, 00:28:27.09 because it wouldn't make sense 00:28:30.10 to be paying attention to its own signal 00:28:33.14 that it's trying to propagate out. 00:28:36.10 So, I've tried to give you 00:28:40.27 a sort of an introduction to clock genes, 00:28:44.13 clock cells, 00:28:46.02 and clock circuits 00:28:48.19 in the circadian system, 00:28:50.24 and I think in the field of neuroscience 00:28:55.07 we're really at a very exciting time today, 00:28:58.25 because the tools of both genetics and genomics 00:29:02.11 are really enabling us 00:29:05.07 to understand how 00:29:08.01 behavior and physiology are really regulated. 00:29:10.14 And we can very easily 00:29:14.28 go all the way from genes, 00:29:17.03 cells, circuits, to behavior, 00:29:19.27 in the circadian system, 00:29:22.28 where we have, 00:29:24.15 correspondingly at these many levels of organization, 00:29:26.19 clock genes, clock cells, clock circuits 00:29:29.19 in the SCN, 00:29:31.21 which then can regulate both physiology and behavior. 00:29:35.01 And it's a very exciting time 00:29:37.24 because both normal behavior 00:29:40.06 as well as pathological conditions 00:29:43.10 might be regulated by this system. 00:29:47.11 So, I'd like to end here 00:29:49.07 and acknowledge all of my colleagues 00:29:51.24 over the many years 00:29:53.26 who contributed to all of this work. 00:29:55.21 Thank you very much.

Part 3: Genetics of Mammalian Clocks

00:00:07.17 Hello. 00:00:09.02 I'm Joe Takahashi 00:00:10.14 at the University of Texas Southwestern 00:00:12.13 and the Howard Hughes Medical Institute, 00:00:16.02 and for this second lecture what I'd like to do 00:00:18.26 is to give you three examples 00:00:21.27 of using genetics in the mouse 00:00:25.06 to really discover genes 00:00:27.21 that are involved in controlling circadian behavior. 00:00:31.03 And so, the way a genetic screen in a mouse 00:00:36.08 is performed 00:00:38.26 is to use a chemical mutagen, 00:00:40.23 such as ENU, or ethylnitrosourea, 00:00:44.11 which is used to treat a male mouse. 00:00:47.17 That causes mutations 00:00:50.12 in the germline of that mouse, 00:00:53.15 and then that treated mouse 00:00:56.05 is crossed to normal female mice 00:00:59.22 in what we call the G0 generation. 00:01:04.17 And these mice 00:01:08.13 then produce a number of offspring 00:01:10.17 that we call G1, or generation 1, mice. 00:01:13.27 These mice are carrying mutations 00:01:17.08 that they inherit from this treated mouse 00:01:20.10 up here. 00:01:22.14 In a recessive screen, 00:01:24.03 which is a little bit complicated 00:01:26.19 because it requires three more generations 00:01:29.22 of crossing, 00:01:31.26 a male mouse is then crossed 00:01:34.06 to another normal female mouse 00:01:35.25 to produce a second generation of progeny, 00:01:40.09 and then the female progeny 00:01:42.27 in this second generation, or G2 generation, 00:01:45.29 are actually backcrossed back to the G1 male. 00:01:50.00 And that produces the third generation of mice, 00:01:53.01 which are called G3s. 00:01:55.02 And the reason we do this 00:01:57.19 is to make any mutation 00:01:59.29 that was heterozygous, or single-copy, 00:02:02.21 in the G1 mouse homozygous, 00:02:06.16 and in this particular procedure 00:02:10.01 we try to screen about 20 G3 progeny 00:02:14.26 for each G1 mouse. 00:02:16.13 That gives us about an 85% chance 00:02:18.17 of detecting a homozygous mutant mouse 00:02:21.21 in the G3 generation. 00:02:24.15 So, in this kind of screen, 00:02:27.14 shown here, 00:02:29.14 where we use circadian rhythms... 00:02:31.24 this particular screen, 00:02:33.23 we screened over 3200 mice, 00:02:35.16 and this histogram here 00:02:37.25 shows you the distribution 00:02:40.11 of period values, 00:02:42.10 similar to what I discussed in the first lecture, 00:02:44.11 for these 3000 mice. 00:02:47.27 What's remarkable about this behavior 00:02:50.26 is how similar 00:02:53.03 each of the mice are to each other. 00:02:55.06 So, the average in this experiment 00:02:57.28 was about 23.2 hours 00:03:00.11 for the whole population of 3000 mice, 00:03:03.12 but the variation within that population 00:03:07.10 was only two-tenths of an hour 00:03:09.16 -- that's only 12 minutes between mice. 00:03:13.23 And so, in this screen... 00:03:17.02 this is again an activity record 00:03:19.21 of a wild type or typically normal mouse, 00:03:22.24 first on a light cycle 00:03:25.02 then transferred to darkness, 00:03:26.19 so we can see its circadian period... 00:03:28.27 we found this mouse, shown in the middle panel, 00:03:32.25 which has a 26 hour clock. 00:03:36.13 And this mutant we named Overtime. 00:03:40.01 It turned out that 00:03:42.11 this mouse didn't breed, 00:03:43.22 but we were able to recover the mutation 00:03:46.12 from a sib, this mouse below, 00:03:48.05 which turned out to be a heterozygous carrier. 00:03:51.24 And using this mouse, 00:03:53.17 we were able to map the mutation 00:03:56.20 to this region of the genome, 00:03:59.21 and once we were able 00:04:01.22 to reduce this interval, 00:04:03.13 we found that there was only 00:04:05.28 a single mutation in this gene, here, 00:04:08.06 which is called Fbxl3, 00:04:11.08 which is an F box protein. 00:04:14.25 Now, an F box protein 00:04:17.10 turns out to be involved 00:04:20.18 in a complex of proteins 00:04:22.16 that are involved in modifying other proteins 00:04:25.14 with ubiquitin chains, 00:04:28.19 and these ubiquitin chains 00:04:31.08 then target that protein 00:04:34.00 for degradation in the proteasome. 00:04:36.29 So, to make a long story short, 00:04:40.11 FBXL3 00:04:43.03 uncovered a new F box protein 00:04:47.00 that targeted the Cryptochrome protein 00:04:51.06 for ubiquitination, 00:04:52.16 and that mark then tags Cryptochrome 00:04:56.04 for degradation. 00:04:58.09 And in the absence of this enzyme, 00:05:00.26 or loss of function of FBXL3, 00:05:03.14 the degradation of Cryptochrome protein 00:05:06.03 is slowed down tremendously, 00:05:09.04 and what that does is to 00:05:11.07 prolong the circadian period, 00:05:15.00 and lengthen the period 00:05:18.01 from 24 to 26 hours. 00:05:20.03 So we can see that this particular step 00:05:23.20 accounts for a few hours 00:05:26.13 out of 24 00:05:28.10 in the feedback circle. 00:05:31.19 So, in a second screen, 00:05:34.22 shown here, 00:05:36.23 using a slightly different strain of mouse, 00:05:39.03 so the period of the rhythm is a little longer 00:05:41.23 -- it's 23.7 hours in this case -- 00:05:46.12 we found a short mutant, 00:05:49.08 and that's shown here. 00:05:51.29 It's only subtly short, 00:05:54.07 about a half an hour short, 00:05:55.29 and we named that mutation Past-time, 00:05:58.28 and when we mapped this gene 00:06:01.25 it turned out to be in a new position, 00:06:05.18 and incredibly this gene 00:06:08.14 turned out to be another F box protein. 00:06:11.12 This time it was called Fbxl21, 00:06:15.11 which is called the paralog of Fbxl3. 00:06:19.24 So, a paralog 00:06:22.10 is a gene that has very similar sequence 00:06:25.12 that arose from gene duplication 00:06:28.06 in the same organism, 00:06:30.09 and so we assumed the Fbxl21 00:06:32.18 should have a related function to Fbxl3, 00:06:37.08 but the mutant is surprising 00:06:39.17 because it has an opposite phenotype. 00:06:41.06 Instead of lengthening the period, 00:06:43.08 as we saw for Overtime, 00:06:45.06 this mutant shortens. 00:06:48.14 And so we looked for 00:06:53.23 the interaction of Overtime, 00:06:56.01 the long period mutant, 00:06:57.18 with Past-time, 00:06:59.29 the short period mutant, 00:07:02.00 by crossing those two strains of mice, 00:07:05.08 and the bottom shows the double mutant 00:07:08.09 and these histograms show 00:07:10.18 the average period, or the period distribution, 00:07:12.21 of these four genotypes of mice: 00:07:16.19 wild type; 00:07:18.05 Overtime mutant, which are 26 hours long; 00:07:20.27 Past-time mutants which are half an hour short; 00:07:23.18 and then the double mutant, 00:07:25.25 Overtime and Past-time. 00:07:27.13 And what you can see is that 00:07:30.11 the Past-time mutant 00:07:32.17 actually neutralizes the period lengthening effect 00:07:35.29 of Overtime 00:07:37.17 and normalizes the period. 00:07:40.06 So this kind of genetic interaction 00:07:42.08 is non-additive, 00:07:44.17 and is interesting because 00:07:46.28 it suggested that Past-time 00:07:49.01 was somehow counterbalancing 00:07:51.15 the effect of Overtime. 00:07:53.22 Now, I'm not going to go into all the details, 00:07:57.18 but I'll just jump to 00:08:00.09 the end of this story, 00:08:03.04 and that was that we found, 00:08:06.20 to our surprise, 00:08:08.25 that FBXL3 is really 00:08:11.28 only found in the nucleus, 00:08:13.22 but FBXL21 is found 00:08:16.28 in both the nucleus and in the cytoplasm. 00:08:20.13 And that in the nucleus 00:08:24.03 there's a very fine balance 00:08:26.29 between FBXL3 and FBXL21 00:08:31.16 interaction for the Cryptochrome protein, 00:08:35.07 and what happens is that FBXL3 00:08:37.12 is normally promoting the degradation 00:08:40.08 of Cryptochrome, 00:08:42.21 but FBXL21 actually competes for that 00:08:46.26 and protects Cryptochrome 00:08:49.05 from degradation, 00:08:51.00 and so that's why we think 00:08:52.25 the two genes have opposite effects. 00:08:55.10 FBXL21 protects CRY; 00:08:58.26 FBXL3 promotes CRY degradation in the nucleus. 00:09:02.18 So they're counterbalancing there, 00:09:05.09 but the surprise is, in the cytoplasm, 00:09:08.21 FBXL3 still degrades CRY, 00:09:10.24 but it does it more weakly... 00:09:13.25 FBXL21 still degrades CRY, 00:09:17.02 but it does so more weakly than FBXL3. 00:09:20.23 So, the discovery of these two mutations 00:09:24.22 in related F box proteins 00:09:27.27 has, first of all, 00:09:30.00 told us that the nucleus is very important 00:09:32.15 in controlling the degradation of CRY, 00:09:36.04 and that the primary effect of these mutations 00:09:39.26 on period length 00:09:41.18 appears to act 00:09:43.21 at the level of the nucleus, not they cytoplasm, 00:09:45.24 so that was a little bit surprising. 00:09:48.12 And the second is that 00:09:51.24 related F box proteins 00:09:55.05 might have very different functions in the cell, 00:09:59.01 both in location but also on their effects on the clock system, 00:10:04.00 even though they look very similar 00:10:05.21 at the sequence level. 00:10:09.19 Okay, so 00:10:13.14 this has really led to a revision 00:10:15.09 of our clock gene network, 00:10:17.10 shown here. 00:10:19.03 This is the core 00:10:21.04 CLOCK/BMAL1/PER/CRY feedback loop, 00:10:25.05 and now we have modified the diagram 00:10:29.13 so that FBXL21 00:10:33.08 is really the primary E3 ligase 00:10:35.20 in the cytoplasm. 00:10:36.24 There is no FBXL3, 00:10:38.07 as we thought before, 00:10:40.03 in the cytoplasm. 00:10:41.18 Instead, most the action 00:10:43.10 is actually occurring in the nucleus, 00:10:46.04 where FBXL21 and FBXL3 00:10:49.28 are competing for CRY 00:10:52.13 in a very fine balance 00:10:54.20 to regulate the stability of the Cryptochrome protein. 00:10:58.13 Okay... so, what I'd like to do now 00:11:02.01 for the third example 00:11:03.23 is to go back to the Clock gene 00:11:08.17 and to tell you about 00:11:10.07 another kind of genetic interaction 00:11:12.19 that we've discovered 00:11:14.27 that involves a much more 00:11:19.08 general background effect 00:11:23.22 of mouse strains 00:11:27.28 on the phenotype of circadian rhythms. 00:11:30.01 So, a number of years ago, 00:11:33.18 we looked at the phenotype of different inbred strains, 00:11:36.17 and this is just giving you an example 00:11:38.21 of two inbred strains. 00:11:40.04 C 57 Black 6J (C57BL/6J), 00:11:42.14 which is the very common inbred strain that we use... 00:11:45.14 it has a beautiful circadian rhythm. 00:11:48.11 Two records are shown here for you, 00:11:51.21 a record from a female on the left 00:11:53.19 and a male mouse on the right. 00:11:55.06 Both of them have very precise, robust rhythms 00:11:57.28 that you can just see visually. 00:11:59.23 And then on the bottom 00:12:01.21 are two records from 00:12:03.20 another inbred strain 00:12:05.21 called BALB/c, 00:12:07.06 and this mouse is very commonly used in the laboratory, 00:12:10.10 but as you can see, 00:12:12.21 its circadian behavior is not so 00:12:15.24 clear-cut as Black 6. 00:12:19.06 The pattern is much more fragmented, 00:12:22.01 the period is shorter, 00:12:23.20 and the rhythm is much less stable. 00:12:28.21 And so, using these two strains, 00:12:32.03 we can cross the strains 00:12:35.10 and then look for genetic features 00:12:38.25 that are inherited from these two strains 00:12:41.14 to affect circadian phenotypes. 00:12:44.07 And again, I'm not going to 00:12:46.27 take you through the details, 00:12:48.10 but in this analysis of these two strains, 00:12:50.12 we found about 14 different 00:12:54.25 regions of the genome 00:12:56.05 that are responsible 00:12:58.00 for controlling different phenotypic aspects 00:13:00.28 of the rhythm 00:13:02.20 between these two strains 00:13:04.10 -- their period, their amplitude, 00:13:07.01 how robust they are, 00:13:09.00 things like that -- 00:13:10.10 and these are all indicated 00:13:12.02 in these oval circles with different colors. 00:13:15.18 And what you can see is they're scattered across the genome 00:13:18.25 and what's interesting is 00:13:21.24 their locations are essentially 00:13:27.10 in regions of the genome 00:13:29.12 that don't contain known clock genes. 00:13:31.09 There's only one case 00:13:35.02 where there's an overlap between a known clock gene, 00:13:38.13 casein kinase 1 epsilon, 00:13:40.17 on chromosome 15, 00:13:41.24 and one of these quantitative trait loci, 00:13:44.08 or QTLs, 00:13:45.29 for rhythms. 00:13:48.19 All the rest of them defined new genes. 00:13:51.12 So, what does that tell us? 00:13:52.20 That tells us that even though 00:13:54.07 we have this clock gene network 00:13:57.10 with a core set of genes, 00:13:59.01 there's still many additional genes 00:14:01.21 that can affect 00:14:05.00 different parameters of the circadian system, 00:14:06.17 and that we don't really know about 00:14:11.05 at the molecular level. 00:14:12.09 So, the final part of my talk... 00:14:14.24 I'm going to focus on 00:14:17.05 trying to identify 00:14:19.05 one of these types of quantitative genes 00:14:24.04 in the mouse genome, 00:14:26.06 using a very specific case. 00:14:31.01 So, when we were 00:14:34.16 mapping the Clock mutation, 00:14:36.29 we used different strains of mice 00:14:41.19 to cross the Clock mutation with in order to have 00:14:45.08 genetic markers that we can follow in these crosses, 00:14:48.20 and in one of the crosses that we made 00:14:51.04 we crossed the Clock mutant mouse 00:14:54.21 to the BALB/c mouse that I just discussed. 00:14:58.15 So, what we have here 00:15:00.28 are records from wild type mice 00:15:04.09 or Clock heterozygous mice, 00:15:07.07 but they're in different genetic backgrounds. 00:15:09.28 The top two are from C 57 Black 6 mice 00:15:12.29 and you can see what I showed you before: 00:15:15.10 the Clock mutation 00:15:18.01 lengthens the period by about one hour. 00:15:19.23 But when we cross to BALB/c, 00:15:23.19 even though this mouse on the right 00:15:26.13 is the Clock mutant, 00:15:28.17 its period looks normal, 00:15:30.26 so we call this genetic suppression. 00:15:33.08 The BALB/c background 00:15:36.07 is somehow suppressing 00:15:38.16 the period-lengthening effect of the Clock mutation. 00:15:40.18 And if we were to make 00:15:42.17 four additional crosses to BALB/c, 00:15:45.16 shown here, 00:15:47.03 we get complete suppression of the Clock mutant, 00:15:50.23 and this is shown here on this graph on the right. 00:15:53.28 In the Black 6 background, 00:15:57.11 we see this period difference, 00:16:00.10 in the F1 we see suppression 00:16:02.24 of about 50%, 00:16:05.14 and then in the four-generation cross to BALB/c 00:16:09.22 we see complete suppression of period. 00:16:13.10 So, that's at the behavioral level. 00:16:16.16 This suppression also occurs 00:16:18.17 at the tissue level. 00:16:20.05 So, shown down here 00:16:22.17 are PER::LUCIFERASE recordings 00:16:25.03 of the suprachiasmatic nucleus 00:16:26.13 and the pituitary gland, 00:16:28.08 and we see that even at the tissue level 00:16:31.02 there is suppression 00:16:34.03 of the period lengthening of Clock 00:16:36.05 in these two tissues, 00:16:38.00 so this is a global effect, 00:16:40.07 not just the behavior. 00:16:43.20 So, how do we find this suppressor gene? 00:16:47.27 So, we can approach this 00:16:50.06 by trying to genetically map the suppressor, 00:16:53.09 and this is done 00:16:56.02 by comparing different types of crosses. 00:16:58.26 So, here's the Clock mutation 00:17:02.14 on a pure B6 00:17:05.23 or C57 Black 6 background, 00:17:07.12 this is the F1 that we saw before, 00:17:10.01 and then these are two crosses 00:17:11.26 that are called backcrosses 00:17:14.20 -- back to either Black 6 00:17:18.11 or BALB/c, shown in green -- 00:17:20.23 and then this lower panel is an F2 cross, 00:17:25.10 which is a cross of two F1 mice 00:17:29.04 of this type here, okay? 00:17:31.24 And what you can see is the period distribution 00:17:35.13 is much broader 00:17:38.15 in the BALB/cN2 00:17:41.04 and this F2 generation, 00:17:43.18 and this is really, we think, 00:17:46.00 due to this genetic diversity. 00:17:47.27 And so what we can do is 00:17:49.22 we can take these F2 mice 00:17:51.14 and type each of the mice, 00:17:54.00 because each mouse is 00:17:56.15 a unique combination 00:17:58.24 of Black 6 and BALB/c 00:18:02.21 DNA in their genome, 00:18:04.17 and we can scan each of their genomes 00:18:07.22 with DNA markers 00:18:09.11 to type what their genotype is 00:18:12.04 across the genome, 00:18:14.05 and then see if there's 00:18:16.22 any particular kind of association 00:18:19.18 between a BALB/c piece of DNA 00:18:22.24 and this period suppression of Clock. 00:18:26.09 And so that's what's done here in this graph, 00:18:29.10 which is scanning the genome 00:18:31.17 with different markers. 00:18:32.19 Each of these numbers 00:18:34.19 represents a mouse chromosome, from 1-19, 00:18:37.21 and then the X chromosome, here. 00:18:40.18 And what this shows you is that 00:18:44.10 on chromosome 1 we find a peak, 00:18:47.10 highly significant, 00:18:49.10 which shows an association with the suppression, 00:18:53.12 on chromosome 1, 00:18:55.16 but really nowhere else in the whole genome. 00:18:58.03 Okay? 00:18:59.14 On the bottom, 00:19:01.01 if we blow up chromosome 1, 00:19:02.21 which is the largest mouse chromosome, 00:19:04.20 it's 200 megabases in size, 00:19:07.19 the red line shows this initial peak. 00:19:09.23 It's very broad, 00:19:11.07 it covers almost half the chromosome, 00:19:14.09 which is 100 megabases; 00:19:16.09 it's impossible to find the gene 00:19:19.01 in such a big region. 00:19:21.08 And then in blue and green 00:19:23.17 are shown additional crosses 00:19:27.24 that try to isolate this region of DNA 00:19:30.18 for many generations, 00:19:31.25 up to 8 generations of crossing, 00:19:34.17 and what we see is that 00:19:37.23 the distal tip of chromosome 1, 00:19:41.17 now, 00:19:42.28 is the region that carries the suppressor. 00:19:44.26 Okay? 00:19:46.01 But unfortunately this region is still huge. 00:19:49.07 And so it's really difficult to find a gene 00:19:56.03 in such a big region. 00:19:57.14 But to prove that that region of chromosome 1 00:19:59.07 really carries the suppressor, 00:20:01.09 what we do is we isolate that piece of DNA 00:20:04.27 on a Black 6 background, 00:20:07.11 so only the BALB/c DNA 00:20:11.01 on chromosome 1, 00:20:12.28 this segment shown in green, here, 00:20:15.03 on this representation of chromosome 1, 00:20:17.17 which is now 25 megabases... 00:20:21.18 this piece of BALB/c DNA by itself 00:20:26.06 can suppress the Clock mutation, 00:20:29.03 as shown in these activity records, here, 00:20:33.02 and in this graph here, 00:20:36.00 the red line, 00:20:37.28 which is showing suppression. 00:20:39.13 So this tells us the BALB/c suppressor of Clock 00:20:43.01 is within this region, 00:20:44.08 because we've completely isolated it 00:20:46.06 on that piece of DNA, 00:20:49.29 but again there are way too many genes 00:20:53.00 to find the gene in 25 megabases. 00:20:55.15 So, how do we go on from this point? 00:21:01.11 It turned out, in 2002, 00:21:03.16 the mouse genome was sequenced, 00:21:07.02 and one of the interesting discoveries 00:21:10.16 in the sequencing of the mouse genome 00:21:12.23 was that when we look at 00:21:17.13 different inbred strains of mice, 00:21:18.27 we find different blocks 00:21:22.13 within the genome 00:21:24.27 that are either similar or very different 00:21:27.24 to C57 Black 6, 00:21:30.02 and that's indicated in these bars, here, 00:21:32.25 where this is a comparison of the strain 00:21:36.05 129 with Black 6. 00:21:37.23 Red shows the differences 00:21:40.15 and light blue show the similarities 00:21:42.15 in those two strains. 00:21:44.03 This is another strain comparison, 00:21:46.02 C3H with Black 6. 00:21:48.14 And then this is BALB/c with B6, 00:21:51.21 the comparison we've been talking about. 00:21:54.03 And what you can see is that 00:21:56.14 there are little blocks of DNA 00:22:00.00 that are interspersed throughout the genome, 00:22:03.22 and so we now know 00:22:06.20 from more recent complete sequencing information 00:22:09.15 that classic laboratory inbred strains of mouse 00:22:13.24 are really hybrids 00:22:18.23 of many different kinds of mice, 00:22:22.01 primarily three wild species of mice: 00:22:25.07 Mus domesticus, shown in blue; 00:22:27.29 Mus castaneus, shown in green; 00:22:29.17 and Mus musculus, shown in red. 00:22:33.01 And, in general, 00:22:36.07 the laboratory mice that we use today 00:22:38.21 are really derived from mouse breeders 00:22:42.20 that domesticated mice 00:22:45.13 both in Asia and in Europe. 00:22:49.17 And these mice were ultimately derived 00:22:53.02 from these different, 00:22:55.09 natural wild progenitor strains. 00:22:57.29 So, inbred strains 00:23:00.23 are really not pure species; 00:23:02.24 they're really mixtures 00:23:05.10 of different species of mice. 00:23:07.22 And so this led to the idea 00:23:12.14 that perhaps the suppressor of Clock 00:23:15.18 might be an ancestral allele 00:23:20.21 that was inherited from one of these 00:23:23.14 different species 00:23:24.29 that contributed to inbred mice. 00:23:27.10 And so, to test this idea, 00:23:30.00 what we decided to do was to 00:23:32.23 cross the mouse Clock mutant 00:23:34.22 to different inbred strains of mice, 00:23:37.03 shown here, 00:23:39.02 and ask, 00:23:41.00 does a different strain of mouse 00:23:43.08 suppress Clock or not? 00:23:45.13 Okay? 00:23:47.11 And the data would look like this... 00:23:48.26 this is a suppressed mouse 00:23:50.07 and this is a non-suppressed mouse. 00:23:52.25 So, Shimomura, 00:23:55.25 who did this experiment, 00:23:57.29 crossed to 15 additional strains of mice 00:24:01.11 and he was able to find that 00:24:06.02 8 strains carried the suppressor for [Clock], 00:24:08.26 shown at the top here, 00:24:10.15 and 7 additional strains, 00:24:13.00 in addition to C57 Black 6, 00:24:14.24 failed to suppress. 00:24:16.28 These strains are shown in the bottom, here. 00:24:19.06 You can see the red lines 00:24:21.03 are horizontal and parallel, okay? 00:24:24.04 So, this experiment 00:24:28.17 confirmed our hypothesis 00:24:32.03 that it was likely that the suppressor 00:24:35.02 in the genetic background of BALB/c 00:24:38.02 was an ancestral allele 00:24:40.18 that was carried by many other 00:24:42.28 inbred strains of mice. 00:24:45.13 And so we could then 00:24:47.15 take advantage of that 00:24:49.29 by looking within the 00:24:52.29 25 megabase suppressor region 00:24:55.23 of chromosome 1 00:24:57.12 and look at all the sequence differences 00:25:00.10 in these 15 additional strains of mice, 00:25:04.14 in comparison to Black 6. 00:25:06.10 So, these histograms show 00:25:07.24 all the sequence differences 00:25:10.02 with Black 6. 00:25:11.08 The top strains, shown in green, 00:25:13.15 are the suppressor strains, 00:25:15.04 and the bottom strains, shown in blue, 00:25:17.28 are the non-suppressor strains, 00:25:20.14 and within this 30 megabase region 00:25:23.20 we could find only one very small interval, 00:25:26.24 shown here in green, 00:25:29.18 that matched perfectly 00:25:31.14 the pattern of suppression 00:25:34.10 and non-suppression 00:25:35.27 among these strains. 00:25:38.04 And so that suggested 00:25:40.29 that the suppressor was restricted 00:25:43.01 to this narrow region, 00:25:45.05 which is about 900 [kilobases] in size, 00:25:49.02 and when we look at this region, here, 00:25:51.09 blown up, 00:25:52.26 we see that there are actually 22 genes there. 00:25:56.23 And so 22 genes 00:25:58.25 is actually a number that we can deal with. 00:26:01.11 We were able to sequence all 22. 00:26:05.05 We could find no obvious mutations 00:26:07.18 that could explain the suppression, 00:26:11.29 and so we had to use different methods. 00:26:14.24 And so, one method that we did, 00:26:17.09 shown here on the left, 00:26:19.03 is to look at the expression pattern 00:26:22.00 of these 22 genes. 00:26:24.04 And because the suppressor of Clock 00:26:27.04 is ubiquitously distributed, 00:26:30.03 like a clock gene, 00:26:31.29 we asked, 00:26:33.12 how many of the 22 suppressor genes 00:26:36.27 have an expression pattern that's common, 00:26:39.16 or similar, 00:26:41.10 to the clock genes? 00:26:42.12 So, the clock genes are shown in yellow, 00:26:47.20 and the candidate genes 00:26:50.11 are shown in light blue and gray 00:26:51.19 in this heatmap representation 00:26:55.06 of gene expression, 00:26:56.12 and we profiled 10 different tissues in the mouse. 00:26:59.15 And what you can see is the clock genes 00:27:01.16 are clustered at the top of this heatmap, 00:27:06.02 because their expression is high and ubiquitous, 00:27:09.08 and seven of the candidate genes, 00:27:11.27 shown in blue, 00:27:14.10 had expression patterns 00:27:16.22 similar to clock genes 00:27:18.17 and clustered with them. 00:27:20.14 The other candidate genes 00:27:23.05 had very different expression patterns 00:27:25.03 and that suggested that they 00:27:28.00 might not be involved. 00:27:29.06 So we focused on these seven genes, 00:27:31.01 they're shown here. 00:27:33.19 This shows their RNA expression 00:27:36.07 over time of day 00:27:39.26 in either an F1 mouse 00:27:41.27 that's a suppressor strain 00:27:43.11 or a Black 6 mouse. 00:27:44.20 Okay? 00:27:46.08 And out of these seven genes, 00:27:48.07 only one of them showed an expression difference. 00:27:50.14 That's this one here, Usf1, 00:27:53.18 or upstream factor 1 gene. 00:27:57.07 And when we looked at the levels 00:28:00.00 of the Per1/2 and Cry1/2 genes, 00:28:03.11 they are also elevated 00:28:05.12 in the suppressor strain. 00:28:09.21 Okay? 00:28:11.09 When we then look at 00:28:13.15 the expression of the USF1 protein, 00:28:17.10 we find that there's 00:28:19.22 a very subtle difference, 00:28:20.27 it's a 40% increase in USF1 protein... 00:28:23.19 very small, but significant. 00:28:26.23 So that focused our attention on Usf1. 00:28:30.10 Usf1 is interesting 00:28:32.20 because it's a transcription factor, 00:28:35.09 and because the Per genes and Cry gene 00:28:39.01 RNAs were elevated, 00:28:40.04 that suggested that 00:28:42.06 maybe their transcription was elevated, 00:28:44.14 and so we tested the 00:28:46.11 seven candidate genes 00:28:48.20 for activation of the Per1 and Per2 genes 00:28:51.15 using promoter assays. 00:28:55.07 And these two graphs show that, 00:28:57.10 of these seven genes, 00:28:59.03 Usf1 strongly activates 00:29:01.24 both Per2 and Per1 00:29:04.19 in a dose-dependent manner, 00:29:07.20 but unlike Clock and BMAL, 00:29:10.24 which also activate Per1 and Per2, 00:29:13.10 Usf1 is not suppressed 00:29:16.25 by Cryptochrome or Period, 00:29:20.10 as shown here, 00:29:22.01 where Clock activation 00:29:24.21 is suppressed by Cry1 or Cry2. 00:29:27.05 This does not occur with Usf1, here. 00:29:30.11 So, this suggests that 00:29:33.03 Usf1 might be acting 00:29:36.01 on the same pathway as Clock and BMAL. 00:29:40.19 But to prove that Usf1 is really the gene, 00:29:45.29 we had to make a transgenic mouse, 00:29:49.04 which overexpressed Usf1 00:29:52.04 just a little bit 00:29:53.25 -- this is the expression level of Usf1, here, 00:29:57.09 in the transgenic mice -- 00:29:59.08 and what we found is that, 00:30:01.19 with this subtle increase 00:30:04.12 in Usf1 expression, 00:30:06.17 these transgenic mice 00:30:09.02 could also suppress the Clock mutation, 00:30:10.29 as shown in these activity records 00:30:12.28 and this bar graph here. 00:30:15.00 So, this kind of experiment 00:30:18.04 shows that Usf1 00:30:20.20 is capable of suppressing Clock 00:30:23.20 and identifies it as the suppressor of Clock. 00:30:28.28 So, how does Usf1 do this? 00:30:33.10 Because we sequenced the Usf1 gene... 00:30:36.18 there are no mutations in Usf1. 00:30:39.13 The only difference is the expression level of Usf1, 00:30:43.02 and so this suggested a regulatory mutation. 00:30:46.13 And so, in mice, 00:30:48.20 you can do a very nice experiment: 00:30:50.07 you can take an F1 mouse, 00:30:52.22 an F1 of Black 6 and BALB/c, 00:30:56.03 and if the RNA has a sequence difference 00:30:59.16 between those two strains, 00:31:01.26 you can actually measure 00:31:04.29 the BALB/c transcript 00:31:06.25 and the Black 6 transcript separately, 00:31:09.02 and so we've done that in three different ways, 00:31:11.14 illustrated in this graph. 00:31:14.25 And all three methods, 00:31:17.04 I won't go through the details, 00:31:19.03 show that in an F1 mouse, 00:31:22.07 the BALB/c allele 00:31:24.22 is preferentially increased in its expression, 00:31:27.07 over the Black 6 allele. 00:31:29.26 And that's really a beautiful experiment 00:31:32.00 because it's the same mouse 00:31:34.21 with two different alleles, 00:31:36.17 and one allele is up and the other is not. 00:31:39.10 So that shows that 00:31:42.05 the regulation of Usf1 00:31:45.04 is cis regulation 00:31:47.20 as opposed to trans regulation. 00:31:52.02 So, how might that work? 00:31:53.29 So, to look at this further, 00:31:56.03 what we did was to isolate 00:31:58.09 the upstream regulatory regions of Usf1 00:32:01.15 and, using reporter gene assays, 00:32:05.05 we could show that 00:32:08.11 the Black 6 upstream region, 00:32:10.02 which is shown in blue, 00:32:12.13 as compared to the BALB/c upstream region, 00:32:15.02 shown in green... 00:32:16.27 there's a slight increase 00:32:19.16 in the transcription from the BALB/c promoter. 00:32:23.17 And within this region, 00:32:25.20 which is about 1 kilobase of the BALB/c promoter, 00:32:29.07 there are only 7 sequence differences 00:32:33.01 in this regulatory region, 00:32:35.04 shown in green here, 00:32:37.02 these 7 SNPs, 00:32:38.27 or single nucleotide polymorphisms. 00:32:41.04 And so we tested them 00:32:43.22 and two of them, 00:32:45.26 SNP3 and SNP7, 00:32:48.18 show a difference in transcription 00:32:50.15 between the two strains, 00:32:53.03 with the BALB/c strain being higher, 00:32:54.29 and when we put SNP3 and SNP7 00:32:58.11 of BALB/c, in green, 00:33:01.00 into the Black 6 background, 00:33:04.01 so there is only one difference 00:33:06.13 in these two constructs, here, 00:33:08.05 we see that SNP7, shown here, 00:33:11.05 can completely account for the difference 00:33:14.02 between BALB/c and Black 6. 00:33:17.09 So this suggests very strongly 00:33:20.24 that the difference between Black 6 and BALB/c 00:33:24.03 is due to a single regulatory change 00:33:27.14 in the promoter of the Usf1 gene 00:33:30.03 that increases the expression of Usf1 00:33:33.16 in the BALB/c mice. 00:33:35.26 Okay. 00:33:37.29 So, how does this subtle increase 00:33:42.10 in Usf1 00:33:44.22 then suppress Clock in a mouse? 00:33:48.14 So, to examine this question, 00:33:50.23 what we did was to 00:33:53.09 go back and try to study 00:33:55.23 the binding of CLOCK/BMAL onto DNA, 00:34:01.23 and to see if USF1 00:34:06.11 might bind the same site. 00:34:08.29 And so for a number of genes 00:34:12.24 that we know are regulated by CLOCK, 00:34:15.10 called Dbp, Per1, and Per2, 00:34:19.01 we could find that 00:34:23.14 CLOCK/BMAL bound 00:34:26.14 to specific regulatory sites 00:34:28.16 that are called E-boxes, 00:34:30.09 which we can detect in this assay 00:34:32.17 which is called a gel mobility shift assay, 00:34:34.20 okay? 00:34:36.11 And so the way this experiment works is, 00:34:38.25 here in wild type mice, 00:34:41.17 we have liver extract 00:34:44.04 that contains many different proteins 00:34:46.07 from the nuclei of liver of mice, 00:34:49.28 and we bind them to 00:34:53.17 an oligonucleotide that contains 00:34:56.07 the E-box sequences from the Dbp gene, 00:34:58.08 and what you see is that 00:34:59.20 we get two major bands. 00:35:02.29 The top band turns out to be CLOCK/BMAL, 00:35:06.08 and this lower band turns out to be USF1. 00:35:09.27 So, indeed, USF1 00:35:13.01 can bind the same site 00:35:15.26 as CLOCK/BMAL and, interestingly, 00:35:19.10 in the Clock mutant, 00:35:21.06 what we see, which is shown on the right-hand side here, 00:35:23.28 the Clock mutant binding changes. 00:35:28.17 So, not only does the 00:35:31.16 affinity of binding change, 00:35:33.01 which I'll show you in a minute, 00:35:35.14 but the nature of binding, 00:35:37.02 whether it binds as a single complex, CB1, 00:35:39.26 or a double complex (CB2), also changes. 00:35:42.15 So, the wild type proteins binds as a double complex, 00:35:46.11 CB2, shown here. 00:35:48.21 The mutant protein tends to bind 00:35:51.15 more as a single complex, shown here, CB1. 00:35:55.14 And then, on the right-hand side, 00:35:57.20 when we quantitate the actual affinity of binding, 00:36:00.20 what we see is, in blue, 00:36:03.01 the wild type CLOCK/BMAL complex 00:36:05.06 has much higher affinity than USF1, 00:36:08.20 which is shown in orange and green, 00:36:12.02 but, interestingly, 00:36:14.20 in the Clock mutant, 00:36:17.01 the binding affinity, which is shown in red, 00:36:19.13 actually goes down to match USF1. 00:36:24.06 So there are a couple things going on here. 00:36:27.23 The first is that the CLOCK mutant 00:36:30.18 is actually reducing the binding affinity 00:36:33.16 of CLOCK/BMAL 00:36:36.21 such that the affinity is much closer to USF1, 00:36:41.16 and under these conditions 00:36:44.06 we think that USF1 might be able to then 00:36:47.05 compete with CLOCK/BMAL. 00:36:48.27 To examine the binding 00:36:52.03 of USF1 and CLOCK/BMAL in more detail, 00:36:55.09 we've used a different method 00:36:58.10 which allows us to look 00:37:00.29 across the entire genome 00:37:03.07 for where USF1 and CLOCK/BMAL 00:37:05.20 might bind. 00:37:07.12 And so, these are UCSC Genome Browser views 00:37:12.03 of USF1, CLOCK, and BMAL binding 00:37:16.20 to the Period1 gene, 00:37:18.26 and out here you can see 00:37:21.15 there's very strong binding at these sites here. 00:37:25.18 Red indicates wild type mice 00:37:27.19 and blue indicates Clock mutant mice, 00:37:30.11 and what you can see is that 00:37:32.21 in the Clock mutant mice 00:37:35.12 USF1 binding is much higher 00:37:37.28 than it is in wild type 00:37:41.09 to this site. 00:37:42.26 This is also true for a different gene, 00:37:45.09 this is Dbp shown in the second. 00:37:47.15 Again, at the top, here, 00:37:50.11 USF1 is much higher in the mutant 00:37:53.14 than in the wild type. 00:37:57.04 And over here for Rev-erbα, 00:37:59.06 you can also see a very big difference 00:38:01.18 in these two samples, here, 00:38:04.06 for USF1. 00:38:06.20 So, if we look genome wide, 00:38:09.21 what we see is that there is significant overlap 00:38:12.25 in the binding in wild type 00:38:16.08 and Clock mutant mice, 00:38:18.00 between USF1 and CLOCK/BMAL, 00:38:20.13 but interestingly in the mutant 00:38:23.03 the amount of binding sites go up. 00:38:25.18 And in particular, if we look at this plot, here, 00:38:29.13 which represents USF1 binding 00:38:32.21 in red, the intensity, 00:38:34.16 we see that the binding intensity 00:38:36.23 is much higher at sites across the genome 00:38:41.04 in the Clock mutant than in the wild type, 00:38:46.09 but there isn't so much different 00:38:49.00 in CLOCK or BMAL occupancy. 00:38:53.09 So this suggests that, indeed, 00:38:56.09 USF1 and CLOCK 00:38:58.20 do interact on a very large scale, 00:39:01.07 across the entire genome, 00:39:03.26 and this could explain 00:39:08.06 how USF1 is actually suppressing 00:39:10.19 the effects of Clock. 00:39:13.05 So, here's an overall summary 00:39:15.21 of what we think is going on. 00:39:19.02 In the BALB/c strain, 00:39:22.07 there are regulatory sequence differences 00:39:26.04 in the USF1 promoter 00:39:29.21 that lead to an increase in USF1 expression. 00:39:34.03 In wild type mice, 00:39:36.26 this doesn't really make much difference 00:39:39.27 because the affinity of CLOCK/BMAL 00:39:42.08 is so high that USF1 00:39:44.21 doesn't compete very well with CLOCK/BMAL. 00:39:47.18 But in the Clock mutant, shown here, 00:39:51.05 CLOCK tends to bind as a single complex, 00:39:54.15 the affinity is lower, 00:39:56.21 and so USF1 can occupy 00:39:59.11 or compete for the same E-box sequences. 00:40:02.20 Now, the Clock mutant protein 00:40:04.22 is deficient in transcription, 00:40:07.11 so USF1 actually 00:40:10.18 can rescue that function 00:40:12.29 because USF1 can actually activate transcription 00:40:16.04 from the same regulatory sequences 00:40:18.16 as CLOCK/BMAL. 00:40:20.19 The only difference is, of course, 00:40:23.11 USF1 is not subject to negative feedback, 00:40:27.00 and in fact that promotes 00:40:29.06 the activation potential of USF1 in addition. 00:40:32.18 And so, we can see in 00:40:35.26 this very complex genetic interaction analysis 00:40:39.17 that additional factors 00:40:45.21 can interact with CLOCK/BMAL 00:40:48.16 at a transcriptional level, 00:40:50.28 compete for binding, 00:40:53.05 and actually replace the function of a mutant CLOCK protein 00:40:56.23 to rescue or suppress that behavior. 00:41:02.28 So, I'll stop there 00:41:05.16 and acknowledge all of the people 00:41:07.24 who contributed to these three different stories 00:41:10.27 shown here. 00:41:12.26 Thank you very much.

Part 4: Molecular Basis of a Clock

00:00:07.13 Hello. 00:00:08.28 I'm Joe Takahashi from UT Southwestern 00:00:10.25 and the Howard Hughes Medical Institute, 00:00:13.06 and in this third lecture 00:00:15.07 I want to introduce you to 00:00:19.10 the biochemistry of CLOCK and BMAL 00:00:21.28 and focus on the functions 00:00:24.27 of those two important clock proteins. 00:00:28.18 As we saw in the previous two lectures, 00:00:31.26 the clock is controlled 00:00:34.20 by a network of interacting genes, 00:00:37.17 with a primary negative feedback loop 00:00:40.28 involving CLOCK/BMAL 00:00:43.09 and the Per and Cry genes, 00:00:45.13 and for this lecture today 00:00:48.01 what I want to do is really focus 00:00:50.19 on what we call the core feedback loop, 00:00:53.04 and in particular focus on CLOCK and BMAL, 00:00:57.23 the transcriptional activators in this system. 00:01:04.03 And so, a number of years ago, 00:01:06.28 we began to focus on the biochemistry 00:01:09.26 of these two clock proteins, 00:01:12.16 CLOCK and BMAL, 00:01:14.18 which are illustrated here 00:01:16.25 in diagrammatic form, 00:01:19.15 and to study them 00:01:21.17 we attempted to express and purify those proteins, 00:01:26.13 and we were able to express 00:01:29.10 truncated forms of both CLOCK and BMAL 00:01:32.29 to high levels that were able to 00:01:36.23 produce crystals, 00:01:38.21 such as these shown here, 00:01:40.17 and using this method 00:01:42.26 we were able to determine 00:01:45.05 the X-ray crystal structure 00:01:47.10 of the CLOCK:BMAL complex, 00:01:49.05 which is shown here on the right, 00:01:52.10 BMAL in blue and CLOCK in green. 00:01:56.13 Interestingly, what we can see are 00:01:58.24 the three major domains, 00:02:00.16 the bHLH, or basic helix-loop-helix domain, 00:02:03.29 the PAS-A domain, 00:02:06.14 and then the PAS-B domain 00:02:08.26 of BMAL in this particular region, 00:02:12.05 and then here the interaction 00:02:15.16 of the two proteins. 00:02:17.14 Interestingly, what we find 00:02:19.22 in the crystal structure 00:02:22.08 is that CLOCK is actually wrapped around BMAL 00:02:24.26 and the interactions of the PAS domains 00:02:28.09 are rather interesting and complex. 00:02:31.26 The next slide just shows you 00:02:35.29 a 3D view of the two proteins 00:02:39.01 and we'll just, 00:02:41.18 in the next couple of slides, 00:02:43.23 take a closer look at the PAS-A domain, 00:02:47.27 followed by the PAS-B domain. 00:02:50.03 So, these are the PAS-A domains 00:02:53.22 of BMAL in blue a 00:02:56.02 nd CLOCK in green, 00:02:58.02 looking at the domains 00:03:00.06 down the long axis of the protein. 00:03:02.16 And what you can see is that 00:03:06.10 there's a helical... 00:03:09.00 a helix that's N-terminal to the PAS domain, 00:03:12.23 which is the rest of the structure, here, 00:03:15.00 and these two N-terminal helices 00:03:18.14 form very interesting interaction interfaces 00:03:24.21 between the complementary PAS-A domains, 00:03:27.10 such that the α-helix of one PAS-A domain 00:03:30.26 interacts with the β-sheet surface 00:03:34.14 of the other PAS domain. 00:03:38.17 This kind of interaction has been seen in bacteria, 00:03:42.08 in this case in the NifL protein, 00:03:44.17 but this is really the first view 00:03:47.10 of the PAS-A domain 00:03:49.10 in a mammalian protein. 00:03:52.02 Even more interesting, 00:03:54.03 however, are the PAS-B domains 00:03:55.23 of CLOCK and BMAL, 00:03:57.14 which are shown here, 00:03:59.04 in which BMAL 00:04:01.28 actually stacks in a parallel fashion 00:04:04.02 with CLOCK, 00:04:06.18 such that the α-helical surface of CLOCK 00:04:11.01 is actually interacting 00:04:13.09 with the β-sheet surface of BMAL. 00:04:15.04 This is a little unusual, 00:04:16.29 because traditionally 00:04:19.04 all other PAS:PAS domain interactions 00:04:22.12 typically involve the β-sheet surfaces 00:04:25.07 of those two subunits. 00:04:26.28 As you can see in this example, here, 00:04:29.14 for the HIF-2α PAS-B 00:04:32.25 interacting with the ARNT PAS-B domain, 00:04:37.29 here, 00:04:39.28 where the β-sheet surfaces of the two proteins 00:04:42.10 are really the interaction interfaces. 00:04:44.29 The next slide shows you 00:04:47.13 a detailed view of this interaction 00:04:50.25 and what you can see is 00:04:52.28 a very interesting 00:04:55.27 tryptophan residue 00:04:58.07 that is part of what's called the HI loop of BMAL, 00:05:02.05 and the surface view 00:05:05.02 you can see that 00:05:08.25 this tryptophan residue inserts into 00:05:10.09 a hydrophobic pocket on the surface of CLOCK 00:05:13.26 to form a very nice kind of 00:05:16.17 lock-and-key interaction. 00:05:19.00 And interestingly 00:05:21.21 this tryptophan residue is conserved 00:05:23.29 not only in BMAL, 00:05:25.25 but in CLOCK as well as the PER proteins, 00:05:29.23 which also have PAS domains. 00:05:32.25 So, this is just 00:05:36.08 an overall summary of CLOCK:BMAL, 00:05:38.20 showing the location 00:05:41.01 of a number of previously identified mutations 00:05:43.21 in these two proteins. 00:05:45.27 We can now place them 00:05:48.03 on the structure 00:05:50.09 and have some better understanding 00:05:53.02 of how they might work. 00:05:54.27 In particular, five mutations 00:05:57.20 that interfere with Cryptochrome binding 00:06:01.23 all map to the HI loop of CLOCK, 00:06:05.08 including that tryptophan residue on CLOCK. 00:06:08.15 And so we see this as really just the first step 00:06:11.26 in an atomic-level understanding 00:06:13.25 of how CLOCK and BMAL might function, 00:06:16.25 and in future work we hope to be able to 00:06:20.17 see how the Cryptochrome protein 00:06:22.26 might interact with the CLOCK:BMAL complex, 00:06:25.18 as well as other components 00:06:28.16 such as the Period proteins. 00:06:31.04 So, for the rest of this talk, 00:06:34.26 what I'd like to do is to focus in on 00:06:39.19 this aspect of the mechanism 00:06:42.09 and that is the genomic targets of CLOCK, 00:06:45.08 and using next-generation sequencing technology, 00:06:51.14 we can interrogate the genome 00:06:55.01 for the binding sites of almost any transcription factor 00:06:59.10 that we can study today, 00:07:01.18 using a method called 00:07:03.20 chromatin immunoprecipitation, 00:07:06.02 followed by next-generation sequencing. 00:07:08.03 So, chromatin immunoprecipitation, 00:07:11.06 also called ChIP, 00:07:14.20 is a way that we can precipitate, 00:07:17.02 using antibodies to DNA binding proteins, 00:07:21.22 specific regions of DNA, 00:07:25.09 which we then can purify, 00:07:27.25 make into libraries, 00:07:29.13 and then sequence. 00:07:30.22 Then we map those sequences back 00:07:33.13 to determine where 00:07:37.17 those proteins were binding in the genome. 00:07:40.20 So, this is a browser view 00:07:43.17 of a single known target gene of CLOCK 00:07:46.09 called Dbp, shown here. 00:07:50.28 And in the Dbp gene 00:07:53.26 we can see three regions where BMAL, 00:07:56.10 in this case, binds to the promoter, 00:07:59.04 the first intron, 00:08:01.07 and the second intron of BMAL, 00:08:03.29 and each of these profiles, shown in blue, 00:08:07.04 are ChIP-seq profiles 00:08:09.27 for BMAL DNA binding 00:08:13.12 taken at different time of day, 00:08:15.06 six different times: 0, 4, 8, 12, 16, and 20. 00:08:18.11 And what you can see the amount of binding 00:08:22.08 is dynamic and changes through the day. 00:08:24.13 It's high between 0 and 12 00:08:26.18 and it's low at 16 and 20, 00:08:30.10 and then this final lane, here, 00:08:32.26 labeled KO, 00:08:35.22 is a knockout mouse control for BMAL, 00:08:38.04 which is a control for antibody specificity. 00:08:41.03 There should not be any binding, 00:08:43.07 as you can see. 00:08:45.03 So that's just one factor at one gene. 00:08:49.08 Here's the same gene, Dbp, 00:08:51.20 but now we're looking at 00:08:56.17 the DNA binding 00:08:59.00 of all the major transcriptional regulators 00:09:01.06 in the core feedback loop: 00:09:02.27 BMAL; CLOCK; 00:09:04.11 NPAS2, which is the paralog of CLOCK; 00:09:08.01 PER1; PER2; 00:09:10.04 CRY1; and CRY2. 00:09:13.02 And what you can see is that the 00:09:15.09 three activators all bind the same sites 00:09:18.06 and they have a very similar temporal pattern. 00:09:20.29 They bind primarily from 0-12, 00:09:23.06 which is the daytime. 00:09:25.08 And then the repressors, 00:09:27.06 PER1, PER2, and CRY2, 00:09:29.08 also bind the same three sites, 00:09:33.06 but their temporal pattern is different. 00:09:35.12 They are binding between 12 and 20, 00:09:38.25 which is the night. 00:09:40.28 And then, interestingly, 00:09:43.23 CRY1 shows the third pattern, 00:09:46.27 where it comes on to bind late at 20, 00:09:49.25 peaks at 0, 00:09:52.07 and then starts falling off at 4. 00:09:54.12 So, that's just one gene. 00:10:00.01 What about a genome-wide view? 00:10:02.09 So, this is a representation 00:10:04.17 of all the binding sites 00:10:06.24 for each of the six major clock proteins 00:10:10.03 across the entire genome 00:10:13.10 in heatmap format. 00:10:15.09 So, in the case of BMAL, 00:10:17.19 shown way over here, 00:10:19.21 there are about 5900 sites in the genome, 00:10:24.04 and the red shows the intensity of binding, 00:10:26.26 and what you can see is that 00:10:30.11 binding is highest between 0 and 12, 00:10:32.16 with a peak around 8, 00:10:34.14 in the case of BMAL. 00:10:36.06 A similar pattern for CLOCK, 00:10:38.02 with a peak at 8. 00:10:39.29 And then the repressors, 00:10:41.29 PER1, PER2, and CRY2, 00:10:44.08 are all binding between 00:10:46.27 12 and 20 across the genome. 00:10:49.04 And then CRY1 has a different pattern, 00:10:54.16 which turns out to be a bimodal pattern 00:10:58.20 -- it has both a 12-hour 00:11:01.05 and a 24-hour binding pattern -- 00:11:03.11 but the primary peak is actually at 0, 00:11:06.11 right here. 00:11:08.09 The other feature which is a little interesting 00:11:10.26 is that the Cryptochrome proteins 00:11:13.16 have many thousands of sites. 00:11:15.14 So, in the case of CRY1, 00:11:17.14 there's 16000 sites in the genome, 00:11:19.15 many more than the 5900 BMAL1 sites. 00:11:24.09 And the PER genes also have... 00:11:27.22 or, the PER proteins also 00:11:29.03 have many binding sites, 00:11:30.27 in the case of PER2, over 7000 sites. 00:11:35.08 So, where are these sites 00:11:37.13 and are they binding to similar sites 00:11:40.28 in the genome? 00:11:42.29 So, this complicated Venn diagram, 00:11:45.06 which is called a Chow-Ruskey diagram, 00:11:47.29 shows the 6-way overlap 00:11:51.25 of CLOCK, BMAL, PER1, PER2, 00:11:53.20 CRY1, and CRY2. 00:11:56.06 In the middle 00:11:59.08 is 1400 sites 00:12:01.21 where all 6 factors bind the same site, 00:12:04.21 6 out of 6, 00:12:06.09 and then around the perimeter 00:12:08.10 are all the various combinations 00:12:10.05 -- 5 out of 6, 4 out of 6, 00:12:12.12 3 out of 6, 2 out of 6 -- 00:12:14.04 and so you can see that 00:12:17.12 that binding pattern is a little bit complicated, 00:12:19.23 but interestingly the other feature of this graph 00:12:22.26 is that we find many sites 00:12:26.08 for CRY1 and CRY2, and also PER2, 00:12:29.03 that are separate, 00:12:32.05 they are completely separate 00:12:34.03 from CLOCK:BMAL, 00:12:36.08 which is interesting 00:12:39.04 because we thought previously that PER and CRY 00:12:42.11 primarily interacted with CLOCK:BMAL, 00:12:44.21 but this analysis is actually consistent 00:12:48.16 with work from two other laboratories 00:12:51.08 that has shown that Cryptochrome and PER 00:12:54.20 can interact with another class of transcription factor proteins 00:12:58.05 called nuclear receptors, 00:13:01.12 which are very pervasive across the genome. 00:13:06.14 So, that was DNA binding occupancy. 00:13:09.21 That doesn't tell us about function, 00:13:12.09 and so to try to assess function 00:13:14.29 we used RNA expression across the genome, 00:13:19.04 and in this case what we did was to 00:13:23.14 use whole transcriptome RNA sequencing, 00:13:26.10 and so whole transcriptome 00:13:28.19 means that we're sequencing total RNA 00:13:32.17 that's just been depleted for ribosomal RNA, 00:13:36.18 and this allows to see 00:13:38.28 both messenger RNA 00:13:41.15 as well as intronic RNA sequences. 00:13:44.09 And so this is a genome browser view of the Per2 gene 00:13:47.26 -- the Per2 gene is on the opposite strand, 00:13:50.20 so it's going this way -- 00:13:53.24 and in this case we're taking time samples 00:13:56.17 every 4 hours over a 48 hour period. 00:14:00.15 Okay? 00:14:02.16 And so, the black histograms 00:14:05.09 pointing down 00:14:07.15 show the PER2 transcripts 00:14:11.17 on this opposite strand, 00:14:14.06 and you can see that they cycle. 00:14:16.00 They're low at 0 to 4, 00:14:17.26 then they peak around 12 to 16, here, 00:14:21.12 then they go down again, 00:14:23.09 they're low at 24 to 28, 00:14:24.26 and then they peak again between 36 and 40. 00:14:29.09 Because this sequencing is strand-specific, 00:14:33.05 we found a very interesting result, 00:14:35.14 shown in red. 00:14:37.12 These are reads on the opposite strand. 00:14:41.28 They reveal an antisense transcript 00:14:44.06 within the Per2 gene, 00:14:46.22 and this transcript is also cycling, 00:14:49.10 except it's high when Per is low, 00:14:53.13 and that's shown in this graph here, 00:14:55.25 where the sense transcript is shown in black 00:14:59.17 and the antisense transcript is shown in red. 00:15:03.16 So, there's an 00:15:06.10 antisense, antiphase transcript of Per, 00:15:08.29 which we don't really know the function of 00:15:12.03 at this point. 00:15:13.21 The other feature in this kind of sequencing experiment 00:15:16.20 is that we can annotate the sequence reads 00:15:22.07 in different ways. 00:15:23.29 We can look at the reads that a 00:15:25.27 re only in introns or the reads 00:15:28.02 that are only in exons, 00:15:30.04 or in the entire gene body. 00:15:32.04 And so, if we look at just the intron reads, 00:15:33.04 shown in blue in this upper graph, 00:15:35.24 we see that they're cycling for Per2, 00:15:38.06 as well as the exon reads shown in red. 00:15:41.20 They're both in phase, 00:15:43.23 but the exon reads of course are higher. 00:15:46.14 So, in this case for the Per2 gene, 00:15:48.16 both the intron and the exon reads 00:15:51.03 are cycling. 00:15:53.08 Why would we want to do this? 00:15:55.00 Well, the intron reads 00:15:57.28 are a representation 00:16:00.13 of pre-messenger RNA 00:16:02.07 and so they allow us to estimate 00:16:04.18 nascent transcription, in this case. 00:16:09.16 So, if we then look across the entire genome, 00:16:12.12 we can ask, 00:16:14.06 how many genes show cycling RNAs 00:16:17.26 in the liver? 00:16:19.14 And these heatmap representations 00:16:23.12 show you two answers to that question. 00:16:25.13 The first is the number of genes 00:16:27.12 with intron cycling RNAs, 00:16:29.23 and then the second 00:16:31.03 is the number of genes with exon cycling RNAs 00:16:34.19 to represent messenger RNA. 00:16:36.10 And what we find is, in the case of 00:16:40.02 intron cycling genes, 00:16:41.14 there are about 1400 that are cycling in the liver, 00:16:45.08 and in the case of exon cycling genes 00:16:47.21 there are about 2000. 00:16:49.10 Okay? 00:16:50.24 Now, the exon cycling genes 00:16:54.10 are distributed in time 00:16:57.29 throughout the day 00:16:59.14 -- this is a phase plot of the peak of those rhythms -- 00:17:03.07 and you can see that the peaks 00:17:06.09 are really spread out to many phases, 00:17:09.16 and this is what we saw over a decade ago 00:17:12.04 with microarray experiments, 00:17:14.26 which of course are interrogating 00:17:16.28 mainly exon RNA sequences. 00:17:19.27 But interestingly, when we look 00:17:22.08 at the intron reads, 00:17:23.27 we find a very interesting result, 00:17:25.15 and that is that there is a coordinated peak 00:17:28.09 of intronic RNA rhythms 00:17:31.06 that peaks at CT15, 00:17:33.09 that's about three hours 00:17:36.24 after the beginning of the night of a mouse, 00:17:40.10 and this coordinated transcription 00:17:42.29 is very interesting. 00:17:44.19 The other feature is that the 00:17:47.17 intron cycling genes 00:17:50.22 and the exon cycling genes 00:17:52.27 do not largely overlap. 00:17:54.26 There are only about 450 00:17:57.16 that have both intron and exon cycling transcripts 00:17:59.27 that are detectable. 00:18:04.03 Okay. 00:18:05.23 So, because of this burst of transcription 00:18:09.08 that's happening at CT15, 00:18:11.15 we were very interested 00:18:13.13 in trying to understand what might be regulating this, 00:18:15.13 and so we wanted to then 00:18:18.19 look at the early steps in transcription 00:18:21.27 by RNA polymerase II, 00:18:24.07 shown up here, 00:18:26.11 and this shows 00:18:29.16 a typical sequence of events 00:18:32.10 in the early stages of transcription 00:18:34.28 where, first, activators such as CLOCK and BMAL 00:18:38.07 bind to regulatory sequences in genes. 00:18:41.14 They they recruit co-activators, 00:18:43.18 such as p300 and CBP, 00:18:47.01 to open up the chromatin 00:18:49.07 and allow the formation 00:18:52.29 of the preinitiation complex 00:18:55.26 by RNA polymerase II. 00:18:57.24 In this case, we can detect 00:19:00.08 this hypophosphorylated Pol II 00:19:02.28 with this particular antibody, 8WG16. 00:19:06.18 Then, one of the first steps in initiation 00:19:10.08 of Pol II 00:19:12.06 is serine 5 phosphorylation 00:19:14.10 of the C-terminal domain 00:19:16.13 and, again, 00:19:18.20 we can detect this form of polymerase 00:19:20.11 with a specific antibody 00:19:22.11 for Pol II serine 5 phosphorylation. 00:19:27.23 So, this is a browser view 00:19:30.13 of the Per1 gene, 00:19:33.04 showing BMAL, PER2, 00:19:36.21 p300, 8WG or Pol II, 00:19:39.27 then the serine-5 Pol II, 00:19:42.26 and CBP. 00:19:44.26 And the first surprise is that 00:19:48.21 Pol II occupancy, 00:19:50.28 across the entire genome in the liver, 00:19:52.29 is highly circadian. 00:19:55.01 So, you can see that here at the Per1 locus, 00:19:57.11 where there's a very strong peak at CT12, 00:20:00.12 which corresponds with the RNA transcription, 00:20:03.03 but if we look genome-wide, 00:20:06.09 at over 7000 sites in the liver... 00:20:10.23 genome-wide, 00:20:13.20 Pol II is being recruited to DNA 00:20:16.07 in a circadian manner. 00:20:17.26 This really quite surprising 00:20:20.00 and quite interesting. 00:20:21.27 And if we look at the timing 00:20:23.25 of the Pol II signal, 00:20:26.03 it occurs at CT14.5, 00:20:29.16 which is just half an hour before 00:20:32.00 that intron RNA peak that we just looked at. 00:20:34.21 So this suggests that perhaps 00:20:36.26 this burst of transcription 00:20:38.29 is really a reflection 00:20:41.15 of new transcription by Pol II 00:20:44.05 across the entire genome. 00:20:47.14 Now, to look at the co-activators 00:20:50.00 p300 and CBP, 00:20:51.21 these show you some heatmap representations of those. 00:20:56.12 p300 is recruited by CLOCK 00:20:58.10 and we see that p300 occupancy 00:21:00.12 is actually high in the morning, 00:21:03.13 CT6 or CT5. 00:21:08.12 CBP, on the other hand, 00:21:10.17 has a very unusual pattern, 00:21:12.12 it's bimodal. 00:21:14.02 There's a bump in the morning 00:21:15.20 and then a very strong peak at night. 00:21:17.06 This is very surprising 00:21:20.04 because CBP is an activator, 00:21:22.27 it's recruited by BMAL1, 00:21:24.22 yet there's a peak at night 00:21:27.06 when BMAL1 is typically not there, okay? 00:21:31.25 So what might that mean? 00:21:33.10 And then the second surprise is 00:21:35.17 when we looked at the serine 5, 00:21:37.08 initiated form of Pol II, shown here, 00:21:40.16 it is also highly circadian 00:21:42.29 across the entire genome, 00:21:44.22 but the phase is different. 00:21:47.21 It peaks at 0. 00:21:49.25 And that phase is actually very similar 00:21:51.27 to the phase of binding for CRY1, 00:21:55.02 and so we looked at the overlap 00:21:57.05 of serine 5 Pol II and CRY1, 00:21:59.23 and we see that there is significant overlap. 00:22:03.01 I'll come back to say what we think 00:22:05.29 that might mean. 00:22:07.16 And in the case of CBP, 00:22:10.06 we found that it interacts 00:22:12.28 with the repressor PER2, 00:22:15.07 which is also very surprising, 00:22:17.20 since CBP is typically a co-activator 00:22:20.04 and PER2 is a repressor. 00:22:22.27 And so, in the case of CBP, 00:22:25.06 we think that perhaps in the morning 00:22:27.23 it interacts with BMAL1 00:22:30.05 as a co-activator, 00:22:32.00 but at night it can interact with PER2, 00:22:36.11 which is a repressor, 00:22:39.07 and perhaps might act as a co-repressor. 00:22:42.10 We don't know if that's true yet. 00:22:44.21 Okay, so 00:22:48.13 because of these genome-wide changes 00:22:51.03 in RNA Polymerase II occupancy 00:22:53.28 across the genome, 00:22:55.26 we wanted to then additionally 00:22:57.22 look at the distribution 00:23:00.01 of histone modifications across the genome, 00:23:04.18 which are traditionally associated 00:23:06.23 with the process of transcription. 00:23:09.02 And so this is a summary of different 00:23:12.03 types of histone modifications 00:23:14.02 that are known, 00:23:16.04 in particular histone methylation and acetylation 00:23:20.24 are well known marks 00:23:23.02 that are associated with transcription 00:23:26.12 and promoter regulatory regions. 00:23:29.18 And so, again using ChIP-seq 00:23:31.22 -- this is a browser view of the Per1 gene -- 00:23:35.06 and to get you oriented here is BMAL, again, 00:23:39.29 then this is an activation mark 00:23:42.21 called histone 3 lysine 4 monomethylation, 00:23:45.10 which you can see is sort of high 00:23:48.20 across the gene body. 00:23:50.14 And then a classic promoter mark, 00:23:53.16 histone 3 lysine 4 trimethylation, 00:23:56.27 seen here, 00:23:59.24 is extremely interesting. 00:24:01.23 It's highly dynamic, 00:24:03.25 it's not there at 0 and 4, 00:24:06.19 peaks at 12, 00:24:08.02 and then falls off. 00:24:10.01 A second promoter activation mark, 00:24:11.28 histone 3 lysine 9 acetylation, 00:24:16.03 also is highly dynamic and circadian, 00:24:20.00 as is the promoter and enhancer mark 00:24:24.01 histone 3 lysine 27 acetylation, 00:24:27.20 shown here in orange. 00:24:30.10 These two elongation marks, 00:24:32.13 histone 3 lysine 36 trimethylation 00:24:36.08 and histone 3 lysine 79 dimethylation, 00:24:40.08 you can see are active across the gene body, 00:24:42.27 but there isn't so clear 00:24:46.23 circadian modulation of those marks 00:24:49.07 as we see with the promoter activation marks. 00:24:52.29 This is a second example at the Dbp gene. 00:24:56.24 Again, you see BMAL here at the top. 00:25:00.21 This is the activation mark 00:25:03.15 lysine 4 monomethylation, 00:25:05.03 and then very striking circadian occupancy 00:25:09.00 of lysine 4 trimethylation 00:25:11.13 and lysine nine acetylation, 00:25:14.26 lysine 27 acetylation. 00:25:18.23 So, histone modifications 00:25:22.23 are clearly regulated on a circadian basis. 00:25:26.17 How pervasive is this kind of modification 00:25:30.21 across the genome? 00:25:32.14 And so, to look at this more carefully, 00:25:35.26 we have profiles 00:25:39.12 for both RNA polymerase II 00:25:41.26 and these three promoter marks 00:25:44.16 at the transcription start site 00:25:46.29 of genes across the genome. 00:25:50.04 And so, here at the bottom 00:25:52.02 are all the 1400 intron cycling genes, 00:25:57.00 and each color of the trace 00:25:59.03 represents a different time of day, 00:26:01.18 as you can see here, 00:26:03.20 and what you can see is both polymerase marks, 00:26:06.14 as well as these histone marks, 00:26:09.14 all are dynamic and cycling throughout the day. 00:26:15.17 This is especially true 00:26:17.27 for lysine 4 trimethylation, shown here. 00:26:22.22 So, these are for the 1400 cycling genes, 00:26:24.26 this is sort of what we expect, 00:26:26.24 but the surprise is if 00:26:29.24 we look at all expressed genes, 00:26:31.20 shown at the top, about 12000 genes, 00:26:35.11 the average of these genes 00:26:38.03 also exhibits cycling. 00:26:40.25 Okay? 00:26:42.19 If we look at the genes that are 00:26:45.17 not expressed in the liver by RNAseq, 00:26:47.09 we do not see any of these marks. 00:26:49.00 The reason we did this is in embryonic stem cells, 00:26:52.11 it's typical for inactive genes 00:26:57.20 to carry RNA polymerase marks 00:26:59.22 with different histone modifications, 00:27:03.15 such as K4 (lysine 4) trimethylation 00:27:06.11 and K27 (lysine 27) trimethylation. 00:27:11.05 We do not see any of this in the liver, 00:27:14.25 these so-called poised genes. 00:27:17.03 And then, finally, 00:27:18.24 these histograms at the bottom 00:27:20.22 show the phase 00:27:23.07 and number of genes 00:27:26.22 that carry significant circadian cycling 00:27:29.21 of these histone modifications across the genome. 00:27:33.12 And the big surprise here 00:27:35.22 is the sheer number of the genes 00:27:38.13 that are cycled, 00:27:40.10 so 4000-5000 genes 00:27:43.18 in the liver genome 00:27:45.07 are carrying histone modification sites 00:27:50.07 that are cycling on a circadian basis, 00:27:52.19 much more pervasive than we ever expected. 00:27:56.24 So finally, 00:28:00.13 these marks are also found 00:28:03.08 at enhancer sites 00:28:05.06 in addition to promoter sites, 00:28:06.25 and so we can look at different types 00:28:08.18 of promoter sites, 00:28:10.29 either defined by BMAL, in this case, 00:28:12.12 or other factors. 00:28:14.15 And what we see is that BMAL occupancies 00:28:16.05 at either promoter or intergenic 00:28:20.22 enhancer sites 00:28:22.12 are cycling in the same way. 00:28:26.00 Polymerase is cycling, of course, 00:28:28.02 at the promoter, 00:28:29.20 but also can cycle somewhat 00:28:31.25 at enhancer sites, 00:28:33.27 and then these two 00:28:36.21 promoter activation marks, shown here, 00:28:41.15 are cycling not only at promoters, 00:28:43.15 but also cycling at enhancers. 00:28:46.11 So, we're seeing this kind of circadian regulation 00:28:49.21 not only at promoter sites in the genome, 00:28:52.15 but also at intergenic enhancer sites. 00:28:57.06 And finally, the biggest surprise 00:29:00.05 is here at the bottom. 00:29:03.07 If we look at those genes in the liver 00:29:05.27 that are expressed, but not cycling 00:29:08.23 at the RNA level, 00:29:10.16 we see that they are still carrying 00:29:13.24 polymerase marks and histone modifications 00:29:16.13 that are cycling. 00:29:18.24 So, it's not really 00:29:21.04 whether the gene is cycling or not 00:29:23.15 at the RNA level 00:29:24.29 that's determining whether RNA polymerase occupancy 00:29:27.28 is circadian or whether histone modifications 00:29:30.03 are circadian. 00:29:32.02 Rather, many more genes 00:29:35.25 across the entire genome 00:29:37.13 are being modulated 00:29:39.23 on a circadian basis, 00:29:41.16 both at the level of RNA polymerase occupancy 00:29:45.10 and histone methylation and acetylation. 00:29:49.14 So, what is really best correlated 00:29:53.08 with occupancy of these factors? 00:29:56.24 And so, it turns out 00:29:59.23 the best predictor of occupancy 00:30:02.26 is whether or not the gene is expressed. 00:30:05.28 So, whether the factor 00:30:09.04 is a circadian transcription factor, 00:30:11.05 a general co-activator, 00:30:12.26 or RNA polymerase II itself, 00:30:17.03 there is a 90% concordance 00:30:19.20 between occupancy and RNA expression 00:30:22.22 from that gene. 00:30:25.01 That's a very strong correlation, 00:30:30.01 which of course is very low 00:30:33.10 for unexpressed genes, 00:30:35.02 but the surprise is that cycling genes 00:30:38.00 are not especially enriched 00:30:40.13 for circadian factors. 00:30:42.28 Rather, the circadian factors 00:30:45.06 are acting more like general transcription factors; 00:30:49.14 they are directly correlated with gene expression, per se, 00:30:52.22 not cycling. 00:30:57.01 So, to try to put this all together, 00:31:01.08 if we try to map out these modifications 00:31:06.25 during the day on a circadian basis, 00:31:10.18 what we see in the liver is an interesting set of changes 00:31:15.02 that are occurring. 00:31:16.14 In the morning, at CT0, 00:31:18.07 we see a new state that we didn't see before, 00:31:20.22 and that is that CLOCK and BMAL 00:31:22.21 are bound to the DNA, 00:31:26.15 yet the gene is not turned on, 00:31:29.01 and we think the reason for that 00:31:31.23 is that CRY1 is also 00:31:34.13 bound to CLOCK:BMAL 00:31:36.00 to repress that complex. 00:31:37.27 The complex can still recruit polymerase II, 00:31:42.02 and so we find initiated RNA Pol II 00:31:45.04 with a serine 5 mark 00:31:48.04 also peaking at 0, 00:31:49.16 so we think that there is actually 00:31:52.00 a CLOCK:BMAL:CRY1 trimeric, 00:31:54.04 repressed complex 00:31:55.20 that's recruited Pol II at 0, 00:31:58.15 then CRY1 repression dissipates 00:32:02.29 and as that occurs 00:32:05.29 we then get a wave of transcription factor occupancy 00:32:09.06 -- BMAL, CLOCK, and NPAS2 -- 00:32:12.08 this is accompanied by p300 00:32:15.23 co-activator recruitment, 00:32:17.12 and we also see K9 acetylation 00:32:20.21 as well as K4 monomethylation activation marks 00:32:24.25 occurring at this activation phase. 00:32:27.17 This is then followed 00:32:31.01 by this phase of nascent transcription, 00:32:34.29 which is accompanied by 00:32:37.28 polymerase II hypophosphorylated 00:32:40.23 Pol II, 00:32:42.04 and then shortly after that 00:32:44.09 we find the beginning of the repression phase, 00:32:46.16 where the repressors 00:32:48.28 all begin to occupy and bind. 00:32:52.16 CLOCK and BMAL occupancy falls off, 00:32:55.15 and then the repression phase wanes, 00:32:58.10 and then we start the cycle over again. 00:33:01.24 So, that's an overall summary 00:33:06.00 of what we think is going on 00:33:08.20 at the transcriptional level 00:33:11.22 in cells in the liver, 00:33:15.24 as far as the circadian clock is concerned. 00:33:18.25 And what is really surprising in this work 00:33:21.26 is that the clock is really 00:33:23.23 not only controlling cycling genes, 00:33:26.02 but is really controlling 00:33:28.07 basic machines of the cells, 00:33:30.06 such as RNA polymerase II itself. 00:33:35.14 The RNA transcription machine of the cell 00:33:37.25 is being recruited on a daily basis 00:33:39.20 across the entire genome of the liver, 00:33:42.27 and that's really a surprise. 00:33:46.13 So, I'd like to acknowledge 00:33:48.25 all the people that are involved 00:33:51.02 in these two major projects: 00:33:52.29 the crystal structure of CLOCK 00:33:55.17 and the genome-wide analysis 00:33:58.16 of CLOCK:BMAL and its target genes.

Talk Overview

Circadian rhythms are an adaptation to the 24 hr day that we experience. Takahashi begins his talk with an historic overview of how the genes controlling circadian clocks were first identified in Drosophila and the cloning tour de force that was required to identify clock genes in mice. He also describes the experiments that resulted in the realization that all cells in the body have a circadian clock, not just cells in the brain.

In part 1B, Takahashi explains that the suprachiasmatic nucleus (SCN) in the brain generates a circadian rhythm of fluctuating body temperature that, in turn, signals to peripheral tissues. Heat shock factor 1 is one of the signaling molecules responsible for communicating the temperature information and resetting peripheral clocks.

In Part 2, Takahashi describes how crossing many mice of different genetic backgrounds allowed his lab to identify several genes that impact the output of the clock gene system through different mechanisms.

Takahashi begins the last part of his presentation with the crystal structures of BMAL1 and CLOCK, the two central activators of clock gene transcription. He goes on to describe how his lab showed that CLOCK:BMAL1 controls the DNA binding activity of transcriptional regulators of not only cycling genes, but also of basic cell functions such as RNA polymerase II occupancy and histone modifications.

Speaker Bio

Joseph Takahashi received his BA in biology from Swarthmore College, his PhD in neuroscience from the University of Oregon, and he was a post-doctoral fellow with Martin Zatz at the National Institutes of Mental Health. He then spent 26 years at Northwestern University where he was a faculty member in the Department of Neurobiology and… Continue Reading

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This material is based upon work supported by the National Science Foundation and the National Institute of General Medical Sciences under Grant No. MCB-1052331.

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